AWS Partner Network (APN) Blog

Continuous Compliance in the Cloud: Automating File Security for Regulated Industries

CJ Sturgess — Wed, 04 Mar 2026 23:33:18 +0000

By: Tim Wikander, Alliance Manager – OPSWAT
By: CJ Sturgess, Partner Solutions Architect – AWS

OPSWAT

Driven by the high value of financial data and personally identifiable information (PII), regulated industries such as banking, financial services, and insurance organizations face a mounting risk of cyber threats. As organizations in these industries continue to modernize and expand their cloud footprints, they often become larger targets for threat actors, despite increased investment in cybersecurity and stronger risk management processes.

In modernizing applications across these regulated industries, the digitization of customer interactions has made file upload capabilities an essential business function. However, this opens a new and expanded attack surface that sophisticated threat actors increasingly target. Financial institutions face particularly acute risks, from ransomware attacks targeting their cloud storage systems to novel, previously undisclosed (zero-day) exploits hidden in seemingly innocent file uploads. As these organizations process thousands of customer-uploaded documents daily, including loan applications, insurance claims, and more, even a single compromised file can lead to breaches, operational disruptions, and compliance violations.

OPSWAT, an Amazon Web Services (AWS) ISV Accelerate Partner, offers a unique solution for these challenges: MetaDefender Storage Security. Through advanced technologies such as Deep Content Disarm and Reconstruction (CDR), multi-scanning, and proactive data loss prevention (DLP), MetaDefender Storage Security safeguards cloud storage systems. By implementing robust file security processes and automation, organizations can maintain continuous compliance while ensuring seamless customer experience in an increasingly complex threat landscape.

The challenge

The acceleration of cloud adoption offers new and unique opportunities for financial institutions to scale their operations and enhance customer experiences. For example, as organizations transition from traditional Amazon Elastic Compute Cloud (Amazon EC2) instances to containerized environments using Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS), they can achieve greater operational efficiency and flexibility in deployment. However, this evolution can introduce complex security considerations, especially around file handling and storage access patterns. This is further compounded by the need to manage sensitive customer data across multiple Availability Zones and Regions while still maintaining compliance with industry regulations. For financial institutions, these may include the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and the Gramm-Leach-Bliley Act (GLBA).

These challenges present a unique opportunity for forward-thinking organizations to reimagine their security posture and compliance strategies. By using advanced file security solutions, such as OPSWAT’s MetaDefender Storage Security, financial institutions can turn these challenges into competitive advantages. Implementing automated, policy-driven security checks across diverse cloud storage systems enhances protection against sophisticated attacks and streamlines compliance processes. Organizations that take this approach can confidently innovate and scale their cloud adoption while maintaining crucial security and regulatory requirements typical of the financial services industry.

Introducing MetaDefender Storage Security

To address these evolving security challenges, OPSWAT has introduced MetaDefender Storage Security (MDSS). This solution gives organizations a containerized, cloud-based approach to file security that aligns with their modern infrastructure requirements. Through a centralized management console, security teams can efficiently orchestrate security policies, monitor scanning activities, and maintain compliance across multiple storage locations. That includes locations such as Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS) volumes, or hybrid environments.

OPSWAT’s MetaDefender Storage Security deploys seamlessly through Amazon ECS or Amazon EKS, which means organizations can scale their security measures in parallel with their infrastructure growth. This flexibility extends to deployment strategies, enabling real-time, on-demand, or scheduled scanning across multiple storage repositories. This software as a service (SaaS)-based solution deploys seamlessly and cost-effectively into your existing infrastructure, without extensive setup or storage administration. Further, this solution supports redundant and distributed architectures for continuous protection, minimizing downtime and maximizing service availability. Get up and running in minutes with ready-to-use protection.

Figure 1: Simplified storage security with MetaDefender Storage Security

Implementation and results

A major US banking institution with $31.6 billion in assets and over 1,850 employees successfully implemented this solution with substantial results. They faced a critical challenge: instituting secure file scanning across multiple accounts, Regions, and organizational units (OUs) while actively undergoing an Amazon EC2 to Amazon ECS or Amazon EKS transition. They used OPSWAT’s MetaDefender Storage Security to deploy a scalable solution that protects their extensive Amazon S3 storage infrastructure, overcome technical roadblocks, and meet strict compliance and performance needs. OPSWAT’s tailored configuration produced a successful deployment for the bank. This included supporting secure access to S3 buckets using AWS PrivateLink, consolidating operations under a single account, and deployment using Amazon ECS or Amazon EKS, which resulted in elastic scaling and better resource efficiency.

OPSWAT took a five-step approach to tailoring the configuration and deployment for the institution:

AWS Identity and Access Management (IAM) role integration with PrivateLink
Centralized resource management
Containerized deployment
Validated quality assurance (QA) environment
Flexible deployment strategy

The following diagram illustrates this five-step approach.

Figure 2: MetaDefender Storage Security tailored configuration

The results demonstrated immediate and substantial improvements in the bank’s security operations. Transitioning to an Amazon ECS or Amazon EKS deployment model enabled rapid rollout of policy and engine updates, leading to 75% faster security update deployments. Additionally, IAM role-based integration greatly reduced API key exposure risk, while AWS PrivateLink helped the bank achieve secure, high-throughput scanning. Comprehensive testing facilitated the successful deployment of MDSS across multiple Regions and accounts while supporting future scaling without the need to rearchitect security workflows.

To learn more about how OPSWAT solved this customer’s unique challenges, read the full case study at OPSWAT Powers Secure, Scalable S3 Access for a Major US Bank.

Benefits and impact

The implementation of MetaDefender Storage Security delivers several key benefits and measurable impact. For AWS customers, implementing MDSS offers a transformative impact on their cloud security operations and business efficiency. The solution’s automated S3 bucket discovery and continuous monitoring capabilities significantly reduce manual overhead while strengthening security posture through multiple layers of protection against advanced file-borne threats. Organizations can benefit from streamlined compliance processes with automated reporting and detailed audit trails, helping to meet regulatory requirements across their cloud infrastructure. MDSS’s flexible deployment methods support containerized deployment for efficient resource utilization, scalability, and high availability. This comprehensive approach enhances security through advanced features, including Deep CDR, multi-scanning, and proactive DLP, delivering real business benefits such as reduced operational cost, improved productivity, and faster deployment. The solution’s seamless integration with existing AWS services and support for hybrid environments means that organizations can maintain robust security controls while continuing to innovate and scale their cloud operations.

Getting started

Getting started with MetaDefender Storage Security is straightforward through the AWS Marketplace, where customers can quickly procure and deploy the solution. The solution’s flexible deployment strategy supports rapid deployment through Amazon ECS or Amazon EKS, while the intuitive management console simplifies the initial configuration of security policies and storage connections. OPSWAT provides comprehensive documentation and technical support to ensure a successful implementation, whether starting with a proof of concept or planning a production deployment. To begin protecting your cloud storage infrastructure, visit the AWS Marketplace listing for MetaDefender Storage Security, where you can review pricing options and launch the solution with only a few clicks. For organizations requiring additional guidance or custom configurations, OPSWAT’s technical teams are available to help design and implement a security strategy that meets your specific compliance and operational requirements.

Check out more AWS Partners, speak with an AWS Partner specialist, or contact an AWS representative to learn how we can help accelerate your business.

OPSWAT – AWS Partner Spotlight

OPSWAT is an AWS Advanced Technology Partner and AWS Competency Partner that provides software solutions to secure and manage IT infrastructure, protecting devices and helping secure digital data flow.

Contact OPSWAT | Partner Overview | AWS Marketplace

Say Hello to 165 New AWS Competency, Service Delivery, Service Ready, and MSP Partners Added in February

Nick Paris — Tue, 03 Mar 2026 19:52:01 +0000

By Nick Paris, APN Marketing Manager – AWS Partner Network

The AWS Partner Network (APN) is a global community that leverages Amazon Web Services (AWS) technologies, programs, expertise, and tools to build solutions and provide services for customers.

The APN has more than 130,000 partners from over 200 countries, with 70% headquartered outside of the United States. Together, partners and AWS provide innovative solutions, solve technical challenges, win deals, and deliver greater customer value.

To achieve APN specializations such as AWS Competency, AWS Service Delivery, AWS Service Ready, and AWS Managed Service Provider (MSP), organizations must undergo rigorous technical validation and assessment of their AWS solutions and practices.

NEW! AWS Competency Partners

To succeed with cloud adoption in today’s complex IT environment and continue to advance in the future, customers can team up with an AWS Competency Partner.

The AWS Competency Program validates and promotes AWS Partners with demonstrated technical expertise and proven customer success in specialized areas across industries, use cases, and workloads. Guidance from these skilled professionals can lead to better business and bigger results.

Team up with our newest AWS Competency Partners:

AWS AI Competency

Applify | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Blue Pearl | APAC | Generative AI Consulting Services
Cloud Combinator | EMEA | Agentic AI Consulting Services
Cloud Life Consulting | NAMER | Generative AI Consulting Services
Cognetiks Consulting | EMEA | Generative AI Consulting Services
dbSeer | NAMER | Generative AI Consulting Services
Digitspot Solutions | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
Effectual | NAMER | Generative AI Consulting Services
EFS Networks | NAMER | Generative AI Consulting Services; Agentic AI Consulting Services
ElectroMech CloudTech | APAC | Generative AI Consulting Services
HEXAWARE TECHNOLOGIES | NAMER | Generative AI Consulting Services
io Digital | EMEA | Agentic AI Consulting Services; Generative AI Consulting Services
JBS Dev | NAMER | Agentic AI Consulting Services
KIIRO | EMEA | Generative AI Consulting Services
N-iX | EMEA | Generative AI Consulting Services
Rebura | EMEA | Generative AI Consulting Services
Synechron | NAMER | Generative AI Consulting Services
tecRacer | EMEA | Generative AI Consulting Services
Trek2Summit | EMEA | Generative AI Consulting Services
爱动超越人工智能科技（北京）有限责任公司 | China | Generative AI Applications
Alice | NAMER | Generative AI Applications; Infrastructure and Data
autobotAI | NAMER | Generative AI Applications
AZLOGICA | LATAM | Generative AI Applications
Boost.ai | EMEA | Generative AI Applications
Cerbrec | NAMER | Generative AI Applications
Deepdub | NAMER | Generative AI Applications
Fenergo | EMEA | Agentic AI Applications; Agentic AI Tools; Generative AI Applications; Infrastructure and Data
ILLUIN Technology | EMEA | Generative AI Applications
John Snow Labs | NAMER | Generative AI Applications
SUPER 8 Studio | China | Generative AI Applications
Terra Security | EMEA | Agentic AI Applications
Tuya Global | China | Generative AI Applications

AWS Automotive Competency

FPT Software | APAC | Connected Mobility
NetApp | NAMER | Product Engineering

AWS Cloud Operations Competency

IAMOPS | EMEA | Operations Management

Consumer Goods Competency

Adastra | NAMER | Supply Chain
Datavail | NAMER | Manufacturing; Supply Chain
XalDigital | NAMER | IT & Digital Transformation; Manufacturing; Marketing; Product Development; Supply Chain
FourKites | NAMER | Supply Chain
Kasada | APAC | IT & Digital Transformation

AWS DevOps Competency

Eficode Oy | EMEA | Consulting Services
Magicorn Technology | EMEA | Consulting Services
Softtek | LATAM | Consulting Services
TemaBit | EMEA | Consulting Services
Bricks Software AS | EMEA | Continuous Integration & Continuous Delivery

AWS Education Competency

Process Street | NAMER | Teaching and Learning

AWS Financial Competency

Glassbox | EMEA | General Financial Services
Glia Technologies | NAMER | Banking

AWS Government Competency

Experis | EMEA | Citizen Services
Insight Enterprises | NAMER | Citizen Services

AWS Healthcare Competency

RISCPoint | NAMER | Consulting Services
Strata Analytics Group | NAMER | Consulting Services

AWS High Performance Computing Competency

NetApp Inc. | NAMER | HPC Management

AWS IoT Competency

iret | Japan | Consulting Services

AWS Life Sciences Competency

Kanda Software | NAMER | Consulting Services
ZiMetrics | APAC | Consulting Services

AWS Machine Learning Competency

Rapyder Cloud Solution | APAC | Consulting Services

AWS Mainframe Modernization Competency

Karsun Solutions | NAMER | Mainframe Workload

AWS Manufacturing and Industrial Competency

Applify | NAMER | Engineering and Design; Enterprise Solutions; Smart Manufacturing
Process Street | NAMER | Smart Manufacturing
Cohesity | NA | Resilience Recovery
Snowflake | NAMER | Content Production; Data Science and Analytics; Direct to Consumer; Media Supply Chain and Archive
Caleidos Media S.A.C. | LATAM | Migration Services

AWS Migration and Modernization Competency

Crayon Software Experts India | APAC | Migration Services
Dakok Company | APAC | Migration Services
LCloud | EMEA | Migration Services; Modernization Services
Modus Create | Scaling AI ROI in Regulated Industries | NAMER | Migration Services
Noveo | EMEA | Migration Services
Opsfleet | EMEA | Migration Services
Select Soluções | LATAM | Migration Services
Sutherland Global | NAMER | Migration Services
Temus | APAC | Migration Services
Trask Solutions | EMEA | Migration Services

AWS Networking Competency

Trustsoft | EMEA | Consulting Services
Console Connect | NAMER | Direct Connect Infrastructure Partners; Direct Connect Integrated Partners; Networking Connectivity

AWS Oracle Competency

Infosys | APAC | Consulting Services
Quantum Integrators Group | NAMER | Consulting Services

AWS Retail Competency

Brighting | EMEA | Consulting Services
Hyundai Futurenet | APAC | Consulting Services

AWS Retail Competency

Mara Labs | NAMER | Intelligent Supply Chain

AWS Security Competency

The Scale Factory | EMEA | Identity and Access Management
Abnormal AI | NAMER | Data Protection
Expel | NAMER | Threat Detection and Response
Heimdall Data | NAMER | Data Protection; Identity and Access Management
Netskope | NAMER | AI Security
Terra Security | EMEA | Application Security
Tevico (Comprinno Technologies) | APAC | Compliance and Privacy

AWS Small and Medium Business (SMB) Competency

Cloud4Partners | EMEA | Small and Medium Business
CloudPiles | LATAM | Small and Medium Business
Compass UOL | NAMER | Small and Medium Business
Loka | NAMER | Small and Medium Business
NHN Techorus | Japan | Small and Medium Business

AWS Travel and Hospitality Competency

Kasada | APAC | Digital Customer Engagement

NEW! AWS Managed Service Providers (MSPs)

The AWS Managed Service Provider (MSP) Program validates AWS Partners with a proven track record and experience providing end-to-end AWS solutions to customers at any stage of the cloud journey, including planning and design, building and migration, operations and support, and automation and optimization.

To achieve this level of specialization, AWS MSP Partners must complete an extensive independent audit to ensure business health and technical capabilities meet a high bar, and they must maintain this status through annual audits.

Say hello to our newest AWS MSP Partners:

AWS Managed Service Provider Program

CACI | NAMER
Cambrian Technologies | EMEA
Concentrix Corporation | NAMER
Crayon | EMEA
Crayon Software Experts India | APAC
Dati | LATAM
Hong Kong Qineasy Technology | China
OpsGuru, a Carbon60 Company | NAMER
Sabytel Technologies | NAMER
Synthesis Software Technologies | EMEA
Tigo Business | NAMER

NEW! AWS Service Ready Products

The AWS Service Ready Program validates software products built by AWS Partners that work with specific AWS services.

To achieve this specialization, AWS Partners must undergo service-specific technical validation by AWS Partner Solutions Architects, including review of architecture, customer documentation, and customer case study details to ensure they follow AWS best practices for integrating their products with specific AWS services.

Explore our newest AWS Service Ready Products:

AWS Lambda Ready Products

Beijing Zeasn Information Technology | China | Runtimes
KNOBS | EMEA | Deployment

AWS Security Incident Response Ready

Obok | LATAM

NEW! AWS Service Delivery Partners

The AWS Service Delivery Program validates AWS Partners that have deep technical knowledge, experience, and proven success in delivering specific AWS services to customers.

Work with our newest AWS Service Delivery Partners:

Amazon API Gateway Delivery Partners

10Pearls | NAMER
Aivar Innovations | APAC
Cloudwalker | EMEA
Insphere Solutions | APAC
Liberty TI | LATAM

Amazon CloudFront Delivery Partners

CLOUD CHARIOTS | EMEA
Nerdshub | EMEA
SCAFE | EMEA
Secure Way Technologies | EMEA
StarOne IT Solutions | APAC
UpperStack | LATAM

Amazon Connect Delivery Partners

Cronos NV – Scale division | EMEA

Amazon DynamoDB Delivery Partners

Bricom | China
Cloudwalker | EMEA
Whistlemind Technologies | APAC

Amazon EC2 for Microsoft Windows Server Delivery Partners

Marlocks Technologies | EMEA
West Loop Strategy | NAMER

Amazon ECS Delivery Partners

allOps Solutions | EMEA | Containers
Galactic Network | APAC | Containers
MakoLab | EMEA | Containers
Romexsoft | EMEA | Containers
XalDigital | NAMER | Compute; Containers; Serverless Computing

Amazon EKS Delivery Partners

ENREAP | APAC
Enreap Systems | EMEA
OpsTeam | LATAM

Amazon EMR Delivery Partners

Quantum Bits | APAC

Amazon QuickSight Delivery Partners

Computer Revolution Africa Group | EMEA
M2. technology and project consulting | EMEA
Necko Technologies | EMEA

Amazon RDS Delivery Partners

Shenzhen Yunwei Internet Information Service | China | Amazon Aurora MySQL; Amazon RDS for MySQL
SHINSEGAE I and C | APAC | Amazon Aurora MySQL
StarOne IT Solutions | APAC | Amazon RDS for Oracle
Stormit | EMEA | Amazon RDS for MySQL

Amazon Redshift Delivery Partners

Comprinno Technologies | APAC

AWS CloudFormation Delivery Partners

CloudZenia | EMEA
Enuaba Technologies | EMEA
Whistlemind Technologies | APAC

AWS Config Delivery Partners

EVOLVENX | LATAM
Stefanini IT Solutions | LATAM
Whistlemind Technologies | APAC

AWS Control Tower Delivery Partners

Reply | EMEA | Migration; Networking

AWS Database Migration Service (DMS) Delivery Partners

Aivar Innovations | APAC
Mayhive | EMEA

AWS Direct Connect (ISV and SI)

ACP Engineering (Formerly 56k.Cloud) | EMEA

AWS Graviton Delivery Partners

广州上鸣科技有限公司 | China

AWS IoT GreenGrass Delivery Partners

ACP Engineering (Formerly 56k.Cloud) | EMEA

AWS Lambda Delivery Partners

Arolit Global Services | APAC
cloudvests | EMEA
Max International | APAC
SDNit | EMEA
Structurit Consulting | EMEA

AWS Security Response Delivery

Ancrew Global Services | APAC

AWS Systems Manager Delivery Partners

DeltaOps | LATAM
Digitspot Solutions | EMEA
EVOLVENX | LATAM

AWS Web Application Firewall (WAF) Delivery Partners

Shenzhen Yunqiao Technology | China

More Value, Greater Profitability for AWS Partners

Our mission is to make the AWS Partner Network (APN) with AWS Marketplace your preferred route to market and empower partners to deliver differentiated value to millions of AWS customers, helping you to win more, win bigger, and win faster.

We’re aligning the AWS Partner experience—including programs, incentives, and enablement—around AWS services and the partner business models: business outcome solutions, technology solutions, managed services, services, and resell.

These enhancements provide you with more relevant and prescriptive guidance, where you need it, and in a consistent and predictable manner. Your profitability, along with helping you drive new business, is our number one goal.

In 2026, we’ll continue to ensure that AWS Marketplace and our AWS Partner programs provide demonstrated pathways to success—helping you drive greater customer value and profitability. There’s an exciting journey ahead, and we’re thrilled to be on it together!

Learn how AWS is transforming the partner experience >>

Code Ocean and AWS transform reproducible scientific research with agentic AI

Ankith Ede — Mon, 02 Mar 2026 21:37:25 +0000

By: Ankith Ede, Solutions Architect – AWS
By: Jake Valsamis, Sr Product Manager – Code Ocean
By: Gokhul Srinivasan, Sr Partner Solutions Architect – AWS
By: Simon Adar, CEO – Code Ocean
By: Daniel Koster, VP of Product – Code Ocean

Code Ocean

Introduction

Cloud computing has transformed the life sciences industry, offering scientists access to vast computational resources: petabytes of storage and high performance compute. Yet this digital transformation comes with a paradox. Effectively and securely using the cloud requires engineering and DevOps skills that fall outside of the core expertise of most scientists. This gap has created pressure on research IT teams, who must balance the demands of infrastructure management and compliance while enabling scientists to focus on research.

As the pace of AI innovation accelerates, new demands are being placed on research IT because the safe and compliant use of these technologies hinges on IT-led governance. Generative and agentic AI accelerate scientific discovery by giving researchers the tools to analyze complex datasets and build workflows at scale using natural language. Although scientists are eager to adopt these tools, research IT must ensure secure implementation and meet both organizational and regulatory standards. Without adequate infrastructure, AI implementations risk security, IP exposure, inconsistent reproducibility, and lack of auditability.

In this post, we explore how organizations can adopt Code Ocean to empower research IT and equip scientists with a scalable, reproducible research platform. We’ll also highlight a real-world case study from the Allen Institute and introduce how research IT delivers secure, compliant access to agentic AI with Trusted Agents, which we recently launched in Code Ocean 4.0.

Scaling research IT with a FAIR and reproducible science platform

Code Ocean is a cloud-centered computational research platform that scientists can use to access cloud resources self-sufficiently while maintaining security, compliance, and cost control. Deployed within the customer’s Amazon Virtual Private Cloud (Amazon VPC) endpoint, Code Ocean provides built-in access to data and scalable compute for researchers to run analyses, freeing research IT from day-to-day support. Code Ocean automates the provisioning of cloud computing, which means scientists can focus on discovery rather than managing infrastructure.

At the core of the Code Ocean platform are integrated computational best practices. Code is automatically versioned with Git, so that every change is tracked and reversible. All analyses and pipelines are designed to be immutable and fully reproducible, which guarantees that any result can be recreated exactly as it was originally generated. Data lineage is captured for every result, providing a transparent record of how each output was produced from its source inputs. By working in Code Ocean all data, analyses, and pipelines are automatically findable, accessible, interoperable, and reusable (FAIR). Code Ocean also inherently supports the tools scientists already use, including RStudio, Jupyter, Code Server, Nextflow, and MLflow, which means business users can operate without dependency on research IT. The platform incorporates cost optimizations by default: storage is automatically tiered, idle compute resources are detected and shut down, and high-resolution budget monitoring provides clear visibility into spend. By embedding these capabilities into its platform, Code Ocean reduces the operational burden on research IT while giving scientists the flexibility to innovate and collaborate at scale.

From risk to readiness with Trusted Agents

Organizations are increasingly exploring the power of agentic AI to automate and accelerate the process of discovery. However, organizations must still consider secure, compliant, and documented usage before applying agent workflows in a highly regulated industry such as life sciences. It was these concerns that motivated Code Ocean’s latest release, Trusted Agents in version 4.0, which now enables research IT to bring secure, auditable AI to their organization. With this major release, Code Ocean provisions and manages AI infrastructure securely within the customer’s virtual private cloud (vpc), keeping sensitive data within the customer’s environment.

Code Ocean uses AWS Batch to run workflow pipelines and system jobs and Amazon Elastic Compute Cloud (Amazon EC2) instances for system services and workers. This provides built-in guardrails so scientists can use generative AI confidently within a compliant, controlled framework and work more independently with intelligent agents that guide analysis and assist with troubleshooting.

Secure AI infrastructure

Built on Amazon Bedrock, Code Ocean 4.0 uses large language model (LLMs) such as Amazon Nova Pro and Claude Sonnet by Anthropic in Amazon Bedrock, and supports agents built using agentic frameworks such as Strands Agents. This approach means that developers and scientists can safely use the latest AI capabilities without compromising security or compliance.

The following architecture diagram shows two EC2 instances with Code Ocean Amazon Machine Image (AMI) handling system services and worker tasks. LLM flows go to Amazon Bedrock. AWS Batch manages pipelines and system jobs, interacting with Amazon Elastic Container Service (Amazon ECS). Data is stored across different AWS data storage services.

Figure 1: Code Ocean AWS architecture for scalable workloads

Aqua: Trusted AI for reproducible science

The release introduces Aqua, a natural language AI agent designed specifically for scientists. Purpose-built to work within the context of Code Ocean, Aqua combines the reasoning power of Claude Sonnet models by Anthropic in Amazon Bedrock with the ability to retrieve platform-specific knowledge and execute actions directly. Using the Code Ocean Model Context Protocol (MCP), Aqua is equipped with 18 specialized tools for managing, searching, and executing tasks across platform resources. To ensure compliance and reproducibility, Aqua operates entirely within Code Ocean’s controlled environment, with several factors that underscore Aqua’s ability to generate fully tracked, reproducible, and accountable results:

Data assets accessed or created by Aqua are immutable.
Code generated by Aqua is executed within a Capsule for version control and reproducibility.
Results include full provenance, making the AI-generated output traceable to its underlying code, data, and environment.
Actions that Aqua performs are attributed to the specific user on whose behalf it operates, maintaining a comprehensive, auditable history.

The following screenshot shows the Code Ocean platform UI. On the left, the user asks Aqua the following question about the protein structure displayed on the right:

“What is the solvent accessible surface area of the 1GFL protein I’m viewing?”

With awareness of what is displayed in the platform UI, Aqua has all the necessary context to answer the question.

Figure 2: Aqua, Code Ocean’s AI assistant, embedded in the platform UI

The following screenshot shows the Aqua UI. On the left, an AI generated result of a single cell analysis is displayed. The right side shows the result’s lineage graph, which traces data flow from input, through capsule execution, to final reproducible result.

Figure 3: Aqua’s automated provenance and lineage tracking for AI-generated results

For developers, the release introduces Cline, a secure, context-aware coding agent embedded directly into Visual Studio Code (VSCode). Preconfigured through Amazon Bedrock, Cline provides LLM-powered coding assistance within a familiar development environment and enables integration of custom MCP servers into the Code Ocean platform.

Allen Institute scaled reproducible research with Code Ocean

Driven by the need for a secure, reproducible research platform, the Allen Institute, a Seattle-based research institute, adopted Code Ocean. Code Ocean accelerated Allen Institute’s transition to AWS and provided scientists with access to a scalable computing environment. By using Code Ocean’s integration with AWS services, researchers now consume over 1.75 million CPU hours and generate more than 150 TB of reproducible results each month. Using Code Ocean to manage scalability, cost controls, and compliance, the institute has scaled its data processing from terabytes to petabytes annually.

“Code Ocean makes it easy for our scientists to do their work reproducibly. New users to the platform can get far with just a little support; this gives our engineers time to focus on domain-specific challenges.”

– Dr. David Feng, Senior Director of Scientific Computing, the Allen Institute for Neural Dynamics

Three years into their partnership, the Allen Institute has already generated a data corpus on Code Ocean of over two petabytes, running over 450,000 computations totaling over 1,500,000 hours. They now support over 250 researchers across disciplines with the part-time effort of only five members of the research IT team.

Code Ocean: The trusted foundation for AI-driven science

The convergence of cloud computing and AI presents both a transformative opportunity and a growing challenge for life sciences organizations. As researchers strive to harness the power of these technologies, research IT needs to ensure security, compliance, and reproducibility at scale. Code Ocean bridges this divide, empowering scientists to innovate independently while allowing research IT to maintain full governance and control. The Allen Institute’s experience shows how research IT can use Code Ocean to support hundreds of researchers and petabyte-scale workloads through automation and easy access to scalable infrastructure.

Code Ocean Trusted Agents extends this foundation, providing scientists with secure, auditable access to AI capabilities within their own AWS environment. Aqua intelligent research assistant accelerates discovery with traceable and reproducible agentic analysis. It also acts as a real-time support agent, diagnosing build issues, interpreting AWS errors, and guiding users through complex workflows without IT intervention. Aqua frees IT teams to focus on infrastructure scaling and scientific innovation by cutting operational costs and boosting self-sufficiency.

Code Ocean delivers a future where every scientific breakthrough is reproducible, where researchers spend 100% of their time on discovery, and AI accelerates rather than complicates the scientific process.

Experience the future of secure, scalable science

Discover how Code Ocean empowers research IT and scientists to innovate faster with Trusted Agents. Book a demo today or visit www.codeocean.com to learn more.

Code Ocean – AWS Partner Spotlight

Code Ocean is an AWS Advanced Technology Partner that provides a Computational Science platform for life science Research and Development teams who want a fast and efficient way to start, scale, collaborate, and reproduce computational research

Contact Code Ocean | Partner Overview | AWS Marketplace

Unlocking API Security with Natural Language: How Salt Security’s MCP Server Transforms Insights into Action

CJ Sturgess — Mon, 23 Feb 2026 16:57:54 +0000

By: Michael Callahan, CMO – Salt Security
B:y CJ Sturgess, Partner Solutions Architect – AWS

Salt Security

APIs are sets of software instructions and protocols that enable applications to communicate and exchange data. Today, they’re the fabric of modern applications, connecting data, services, and, more recently, AI agents. Yet for many organizations, understanding the state of their API security can be a challenge. Security teams often face questions such as:

Which of our APIs are most at risk right now?
Which APIs process sensitive information such as personally identifiable information (PII) or financial data?
What policies are being violated that create posture gaps across our environment?
What’s the most effective and efficient way to fix them?

Traditionally, answering these questions requires specialized expertise, sifting through dashboards, and correlating logs and reports across multiple tools. This slows down remediation and keeps critical insights siloed within small security teams.

Salt Security, an AWS ISV Accelerate partner, aims to remove that complexity with the introduction of the Salt Model Context Protocol (MCP) server.

A breakthrough with MCP servers and natural language interaction

Salt Security’s new MCP server brings a fundamentally new way to engage with API security data: natural language. By connecting Salt’s solution with large language models (LLMs) through an MCP server, organizations can ask direct, non-technical questions in everyday language and instantly receive authoritative answers.

Imagine typing queries such as:

“Show me the APIs with the highest risk scores in the last 24 hours.”
“List APIs that expose sensitive customer data and aren’t governed by policy.”
“What policy gaps leave my APIs unprotected, and how do I fix them?”

Instead of spending several hours sifting through logs and reviewing your API security posture, the Salt MCP server delivers answers in seconds. More importantly, it goes beyond surface-level visibility to provide explanations and remediation guidance, making insights actionable for both security and development teams alike.

Customers can also use Pepper AI, Salt Security’s AI-powered natural language interface, to receive actionable insights about their API landscape, posture gaps, and risk. The following screenshot shows using natural language to query the API environment in the Salt MCP server.

Figure 1: The Salt Ask Pepper AI interface showing natural language querying capabilities for API security analysis

Solving the three core API security challenges

There are three central API security challenges that enterprises must address. They need to reduce the amount of time it takes to identify vulnerabilities, find a way to democratize API security knowledge, and make it possible for security teams to make faster, more informed decisions.

In security, every second counts. Traditional methods require analysts to parse raw logs, query dashboards, and build manual reports to pinpoint vulnerabilities. Salt’s MCP-powered natural language interface removes that friction, accelerating time-to-insight and enabling security teams to move at the speed of modern threats.

API security expertise is typically limited to a small group of specialists. With natural language interaction, stakeholders from developers to compliance teams can query Salt for insights without needing deep technical expertise. This democratization brings API security to the forefront across the organization, not only within the security operations center (SOC).

When posture gaps are identified, speed and clarity matter. Salt doesn’t merely flag the gap, it explains why the policy is in violation, what the associated risks are, and what steps can close the exposure. As a result, teams can act with confidence and align remediation with compliance and governance requirements.

Why this matters for AWS customers

For organizations running on AWS, the benefits multiply because Salt’s MCP server integrates seamlessly into cloud-centered environments. Customers can:

Maintain compliance posture using AWS security and data governance frameworks, including the AWS Well-Architected Framework and AWS Cloud Adoption Framework: Governance Perspective.
Simplify reporting for audits with natural language queries like “Which APIs with sensitive data lack encryption policies?”
Protect critical workloads by closing posture gaps faster and with less friction.

AWS customers can now bridge the divide between complex, multi-service API environments and actionable security intelligence, all while staying aligned with their cloud-first strategies.

Getting started with Salt Security’s API protection platform and MCP server

Getting started with Salt Security’s API Protection Platform is straightforward and flexible. Organizations can quickly procure Salt’s solution through AWS Marketplace, streamlining the purchasing and deployment process. For AWS customers, Salt’s Cloud Connect for AWS enables rapid integration with their cloud environment, typically taking only minutes to begin discovering and analyzing APIs. This automated connection alleviates the need for complex manual configurations or agent deployment, providing immediate visibility into API risks and vulnerabilities. As soon as the system is connected, teams can use the power of the Salt MCP server to start querying their API security posture using natural language, making advanced API security capabilities accessible across the organization.

The following screenshot shows the Salt Connector Hub, with its seamless connection to AWS.

Figure 2: Salt Connector Hub dashboard showing AWS environment integration options

The future of API security: From data to dialogue

With the rise of AI agents and LLM-driven workflows, security tools must evolve from static dashboards to interactive, conversational patterns. Salt Security’s MCP server represents that evolution. By empowering organizations to speak to their API security fabric in natural language, Salt is transforming the way teams can discover, govern, and protect their APIs.

The result? Faster insights, stronger security posture, and a security capability that scales with both the complexity of your environment and the pace of AI-driven innovation.

Check out more AWS Partners, speak with an AWS Partner specialist, or contact an AWS representative to learn how we can help accelerate your business.

Salt Security – AWS Partner Spotlight

Salt Security is an AWS Advanced Technology Partner and AWS Competency Partner that delivers an API Threat Protection solution focused on securing the ubiquitous APIs connecting everything from web and mobile applications to microservices and IoT devices. These are the APIs that you develop and own and are at the core of connecting your applications and data.

Contact Salt Security | Partner Overview | AWS Marketplace

How to manage peak traffic on AWS using Queue-it’s virtual waiting room

Jasmine Chua — Thu, 19 Feb 2026 16:29:15 +0000

By: Jasmine Chua, Sr. Account Manager – AWS
By: Alexander Guensche, Sr. Solutions Architect – AWS
By: Martin Larsen, Distinguished Product Architect – Queue-it
By: Mojtaba Sarooghi, Distinguished Product Architect and Engineering Manager – Queue-it
By: Christian Schøning, Head of Content & Product Marketing – Queue-it

Queue-it

Peak traffic events can drive website visits to spike 2 to 25 times, as experienced by Rakuten France and Ingresso, beyond normal levels within seconds, whether from planned launches or unexpected viral attention. While standard AWS best practices like auto scaling, load testing, and caching form essential foundations, they might not fully address scenarios where traffic arrives faster than capacity can provision, backend systems hit scaling constraints, or pre-scaling costs become prohibitive. Additionally, organizations running limited-inventory events face a fairness challenge that infrastructure scaling alone cannot solve.

A virtual waiting room provides a different approach. Unlike load balancers that distribute requests or distributed denial-of-service (DDoS) mitigation that filters threats, a waiting room is fundamentally a user experience solution that controls the rate at which visitors enter applications. This post explores how Queue-it, an AWS Partner, integrates with Amazon CloudFront to add traffic control capabilities while customers maintain their existing AWS architecture.

Understanding the challenge of peak traffic

Peak traffic moments span across industries and can be both planned and unplanned. They range from major product launches and concert ticket sales to unexpected media attention or viral marketing campaigns. Ecommerce organizations face product drops and flash sales. Ticketing companies manage concert sales and venue bookings. Public sector agencies handle registrations for services such as immigration, tax filing, benefit distribution, and parks and recreation access. Educational institutions navigate enrollment periods and housing registrations. Financial services providers experience payday and campaign-driven traffic spikes. Telecommunications companies prepare for device launches.

Real traffic patterns frequently diverge from modeled scenarios, introducing load characteristics that weren’t anticipated during testing. Automatic scaling mechanisms respond to observed load, but when traffic spikes faster than new capacity can provision, a gap emerges between demand and available resources. Backend dependencies like databases and payment processors often have inherent scaling limitations that make them bottlenecks even when frontend infrastructure scales successfully. The cost implications of pre-scaling infrastructure for short-duration events can significantly impact budget planning, particularly when peak magnitude remains uncertain.

Figure 1: A Queue-it customer saw traffic jump 561% within two minutes of a media mention, with spikes often reaching 2–25× normal levels.

Beyond technical considerations, organizations running limited-inventory events face an additional challenge: fairness. Without traffic control mechanisms, high-demand sales can become races measured in milliseconds, often dominated by automated bots rather than genuine customers, resulting in poor customer experience and damaged brand trust.

The business impact of inadequately managed peak traffic can be significant. According to industry research on downtime, 93% of enterprises report costs exceeding $300,000 per hour, while the Global 2000 experiences an average of $23 million annually in productivity losses and overtime wages, plus $11 million in additional infrastructure spending. From a customer experience perspective, 77% of customers will leave a site without transacting if they encounter an error, and one in three will leave a brand they love entirely after only one bad experience.

How Queue-it integrates with AWS architecture

Queue-it provides a managed software as a service (SaaS) virtual waiting room that manages visitor flow during high-traffic periods before giving them access to protected parts of applications at a controlled rate, preventing system overload and ensuring fair access for visitors. The solution operates as a control layer at the edge, integrating seamlessly with existing AWS infrastructure without requiring application re-architecting. The integration works through CloudFront using AWS Lambda@Edge functions to intercept and evaluate traffic before it reaches origin servers.

The integration centers on two lightweight Lambda@Edge functions running at ViewerRequest and ViewerResponse events within an existing CloudFront distribution. This edge-based approach means traffic evaluation happens at AWS edge locations closest to users without introducing latency to origin infrastructure. Configuration requires no DNS changes. An initial prototype integration, including basic testing, can be completed in less than an hour, with full production rollout and validation typically achieved within a day.

When a request arrives at a CloudFront distribution, the Queue-it Connector running as a Lambda@Edge function evaluates whether that request targets a protected resource and whether the visitor has a valid Queue-it token signaling they’ve already passed through the waiting room. Protection rules can be defined for entire sites, specific pages, or actions such as checkout, based on flexible configuration criteria like request URL, user agent, or HTTP header.

When a request requires protection and the visitor lacks a valid token, Queue-it Connector responds with an HTTP 302 redirect to send the visitor to waiting room infrastructure hosted and managed within Queue-it’s own AWS environment. All waiting room logic—including queue management, visitor handling, real-time reporting, monitoring, and rendering of the queue page—occurs outside the customer’s infrastructure, requiring no management effort and consuming none of the customer’s AWS resources. When it’s the visitor’s turn, another HTTP 302 redirect returns them to the site with a signed Queue-it token that grants access. The token remains valid for a configured duration, so the visitor can complete their journey navigating the application normally.

Figure 2: The Queue-it Connector checks incoming requests at the CloudFront edge, temporarily redirecting (HTTP 302) visitors to the waiting room when required, based on configured rules.

Architecture using AWS services

Queue-it’s virtual waiting room is built using AWS services to provide scalability and high availability. The architecture spans multiple AWS Regions and Availability Zones to support data residency requirements and performance optimization. Amazon DynamoDB serves as the primary datastore for queue state management, providing low-latency, high-throughput access for tracking session states and user positions in real time, scaling to handle hundreds of thousands of transactions per second.

Application Load Balancer and Amazon Elastic Compute Cloud (Amazon EC2) work together to distribute traffic intelligently across compute resources; delivering built-in fault tolerance, high availability, and seamless scalability. AWS Fargate provides the foundation for running containerized tasks without server management overhead. Additionally, Queue-it relies on AWS services to provide its core functionality: Amazon API Gateway for API security, Amazon Route 53 for DNS routing, Amazon CloudWatch for monitoring, and AWS Shield Advanced for advanced threat protection. To deliver visitor traffic monitoring and reporting as part of its core features, Queue-it uses Amazon Kinesis for scalable data streaming and Amazon OpenSearch Service for advanced search and visualization.

Figure 3: Queue-it is built on more than 10 core AWS services and runs all waiting room logic, queue management, and visitor handling on its infrastructure.

Operating capabilities

Real-time monitoring and control capabilities are essential for managing peak traffic events. Queue-it uses Kinesis and OpenSearch Service to enable real-time traffic monitoring and control through a web-based admin interface and REST APIs. During an active event, administrators can adjust traffic throughput based on system performance under load, update waiting customers with real-time messages, and pause the queue process and communicate with visitors if unexpected issues arise. These control mechanisms are supported by real-time traffic analytics dashboards, where administrators can monitor key metrics including arrivals per minute, current queue length, and average wait times.

Figure 4: The Monitor page in Queue-it’s web-based admin platform provides real-time metrics and can be used to adjust throughput based on backend system performance.

The customer experience during peak traffic shifts from encountering crashes and errors to receiving controlled access with clear communication. Queue-it provides first-in-first-out access through centralized request management or randomized access options for limited-inventory scenarios. Waiting visitors receive information including queue position, expected wait time, and progress indicators. Email notifications can alert visitors when their turn arrives. Visitors can queue across devices, and customizable branded waiting room pages maintain brand presence throughout the experience.

Figure 5: Visitors enter a branded waiting room that sets expectations, provides real-time updates, and shows their progress.

Customer examples

Sky Mobile, a UK telecommunications provider, used Queue-it’s virtual waiting room for their 2024 iPhone launch, achieving a 37% year-over-year increase in conversion rate to their previous solution and higher sales year-over-year despite smaller relative demand. By using the real-time visibility and control capabilities, they could optimize the event dynamically, running the waiting room for slightly more than an hour compared to 3 hours in 2023.

“The insights Queue-it provides into traffic and the level of control it gave us made a dramatic difference,”

– Kevin Lau, Service Manager at Sky Mobile

“If you have peak demand scenarios like an iPhone launch, Queue-it is really a no brainer,”

– Tom Grammer, Head of Mobile Services at Sky Mobile

His Majesty’s Passport Office processes 4–8 million passport applications annually, with 90% submitted online. To manage traffic spikes that were overwhelming their systems, the team scaled up their cloud infrastructure and even built an in-house waiting room, but the costs and risks were too high. Then they integrated Queue-it using the Connector for Amazon CloudFront and have successfully handled traffic spikes of nearly 70,000 applications while maintaining system stability and avoiding major outages.

“Queue-it really gives us and the business the confidence we need,”

– Elaine Salveta, Digital Services Manager at His Majesty’s Passport Office

“What we’re paying for with Queue-it is safety and reliability, and that’s paid off 100 times over,”

– Kevin Lewis, Product Manager at His Majesty’s Passport Office

Getting started

AWS customers interested in exploring virtual waiting room solutions for peak traffic management can find Queue-it on AWS Marketplace for streamlined procurement. To learn more, visit Queue-it AWS virtual waiting room.

Queue-it – AWS Partner Spotlight

Queue-it is an AWS Validated Technology Partner and a global leader in virtual waiting room technology. Trusted by more than 1,000 organizations and managing over 30 billion visitor interactions annually, Queue-it helps ensure stable operations during peak demand, orchestrating traffic to prevent overload, block bad bots, and improve customer experience. Built on AWS, Queue-it combines deep domain expertise and 24/7/365 global support to help major retailers, ticketing companies, government agencies, and financial institutions run mission-critical high-traffic online events with confidence.

Contact Queue-it | Partner Overview | AWS Marketplace

Say Hello to 224 New AWS Competency, Service Delivery, Service Ready, and MSP Partners Added in January

Nick Paris — Wed, 18 Feb 2026 21:30:01 +0000

By Nick Paris, APN Marketing Manager – AWS Partner Network

The AWS Partner Network (APN) is a global community that leverages Amazon Web Services (AWS) technologies, programs, expertise, and tools to build solutions and provide services for customers.

NEW! AWS Competency Partners

To succeed with cloud adoption in today’s complex IT environment and continue to advance in the future, customers can team up with an AWS Competency Partner.

Team up with our newest AWS Competency Partners:

AWS AI Competency

AllCode | NAMER | Generative AI Consulting Services
Armakuni | NAMER | Agentic AI Consulting Services
Cloudar | EMEA | Generative AI Consulting Services
CloudiQS | EMEA | Agentic AI Consulting Services
Codex | APAC | Generative AI Consulting Services
EDT&Partners | EMEA | Generative AI Consulting Services
HIBERUS IT DEVELOPMENT SERVICES | EMEA | Generative AI Consulting Services
Horizon Digital | APAC | Generative AI Consulting Services
Horus Technologies | NAMER | Generative AI Consulting Services
Idexcel | NAMER | Generative AI Consulting Services
Indra Sistemas (Minsait) | EMEA | Generative AI Consulting Services
INIT | APAC | Generative AI Consulting Services
Integra Technologies | EMEA | Agentic AI Consulting Services
Jump Cube | NAMER | Agentic AI Consulting Services
Kyndryl | NAMER | Generative AI Consulting Services
Lambert Labs | EMEA | Generative AI Consulting Services
Nebulane | EMEA | Generative AI Consulting Services
NeenOpal Inc. | NAMER | Agentic AI Consulting Services
Nextlink Technology Co. | China | Generative AI Consulting Services
Ollion or 2ND WATCH | NAMER | Generative AI Consulting Services
Royal Cyber | NAMER | Generative AI Consulting Services
TEKsystems Global Services | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
UsefulBI Corporation | NAMER | Generative AI Consulting Services
zeb | NAMER | Agentic AI Consulting Services
Cloudzero | NAMER | Generative AI Applications
Coder Technologies | NAMER | Agentic AI Tools
Pier Cloud | LATAM | Generative AI Applications
Process Street | NAMER | Generative AI Applications
Redis | NAMER | Infrastructure and Data
Tealium | NAMER | Infrastructure and Data
ZAFRAN SECURITY | EMEA | Generative AI Applications

AWS Cloud Operations Competency

11:59 | NAMER | Cloud Financial Management; Cloud Governance; Compliance & Auditing; Monitoring & Observability; Operations Management
B8 ICT Solutions | APAC | Cloud Governance
DreamSquad Tecnologia | LATAM | Cloud Financial Management
Out.Cloud | EMEA | Cloud Governance

AWS DevOps Competency

7EDGE | APAC | Consulting Services
adesso SE | EMEA | Consulting Services
Ancrew Global Services | APAC | Consulting Services
Andersen | EMEA | Consulting Services
Apexon | NAMER | Consulting Services
CloudifyOps | APAC | Consulting Services
LIMONCLOUD | EMEA | Consulting Services
Akeyless Security | EMEA | DevSecOps

AWS Digital Sovereignty Competency

Kyndryl | NAMER | Consulting Services
PricewaterhouseCoopers | NAMER | Consulting Services
SnapSoft | NAMER | Consulting Services

AWS Education Competency

Kunduz | EMEA | Teaching and Learning

AWS Financial Competency

AllCloud | NAMER | Banking; Capital Markets; General Financial Services; Insurance
Indra Sistemas (Minsait) | EMEA | General Financial Services

AWS Government Competency

CC Tech Digital | NAMER | Citizen Services; Public Safety
Veterans Engineering | NAMER | Citizen Services

AWS Life Sciences Competency

Benchling | NAMER
Quilt.Bio | NAMER

AWS Mainframe Modernization Competency

Thoughtworks | NAMER | Mainframe Workload

AWS Microsoft Workloads Competency

Futran Solutions | NAMER | Application Modernization

AWS Migration and Modernization Competency

Clouxter | LATAM | Migration Services
Ness A.T | EMEA | Migration Services
Netsync Network Solutions | NAMER | Migration Services
Operisoft Technologies | APAC | Migration Services
OpsTree Global | APAC | Migration Services

AWS MSSP Services Competency

F9 INFOTECH | EMEA | Infrastructure Security; Workload Security

AWS Networking Competency

upd8 | LATAM | Consulting Services
Cohesity | NA | Resilience Recovery
ControlMonkey | EMEA | Resilience Recovery
Gremlin | NAMER | Resilience Operations

AWS Security Competency

Digico Solutions | EMEA | Identity and Access Management
Trustsoft | EMEA | Identity and Access Management
1Password | NAMER | Infrastructure Protection

AWS Small and Medium Business (SMB) Competency

Cloutive Technology Solutions B.V. | EMEA | Small and Medium Business
Criptonube – Myappsoftware | LATAM | Small and Medium Business
Dedalus | LATAM | Small and Medium Business
Dualboot Partners | NAMER | Small and Medium Business
Dudobi | EMEA | Small and Medium Business
ForceOne | LATAM | Small and Medium Business
Ibexlabs / Labra | NAMER | Small and Medium Business
Lambert Labs | EMEA | Small and Medium Business
Optimum Healthcare IT and eCloud Managed Solutions | NAMER | Small and Medium Business
Smile IT Solutions | EMEA | Small and Medium Business
XalDigital | LATAM | Small and Medium Business

AWS Storage Competency

Nubinix Technologies | APAC | Consulting Services

AWS Travel and Hospitality Competency

Phrase | EMEA | Digital Customer Engagement

NEW! AWS Managed Service Providers (MSPs)

Say hello to our newest AWS MSP Partners:

AWS Managed Service Provider Program

Escala24X7 | LATAM
FCN | NAMER
Futran Solutions | NAMER
InfusionPoints | NAMER
Innovative Solutions | NAMER
Lentech | NAMER
Mantalus | APAC

NEW! AWS Service Ready Products

The AWS Service Ready Program validates software products built by AWS Partners that work with specific AWS services.

Explore our newest AWS Service Ready Products:

Amazon CloudFront Ready Products

太美医疗科技 | China | Security
北京生数科技有限公司 | China | Media Management
北京尽微致广信息技术有限公司 | China | Media Management; Monitoring & Analytics; Security
Zenlayer | NAMER | Media Management; Monitoring & Analytics

Amazon RDS Ready Products (Business Applications & Tooling)

浙江数新网络有限公司 | China | Business Applications
北京咖瓦信息技术有限公司 | China | Business Applications
北京尽微致广信息技术有限公司 | China | Business Applications

AWS PrivateLink Ready Products

SINGDATA CLOUD | NAMER

AWS Web Application Firewall (WAF) Ready Products

北京尽微致广信息技术有限公司 | China | Advanced Threat Detection & Mitigation; Logging/Analytics; WAF Managed Rules
Fuama Technologies | EMEA | Advanced Threat Detection & Mitigation

NEW! AWS Service Delivery Partners

The AWS Service Delivery Program validates AWS Partners that have deep technical knowledge, experience, and proven success in delivering specific AWS services to customers.

Work with our newest AWS Service Delivery Partners:

Amazon API Gateway Delivery Partners

Enuaba Technologies | EMEA
HexaIO Kft. | EMEA

Amazon CloudFront Delivery Partners

菲诺氪斯（重庆）科技有限公司 | China
神灏（北京）云计算科技有限公司 – Shenhao (Beijing) Cloud Computing Technology | China
Atyos | EMEA
HexaIO Kft. | EMEA
Japan Open Systems | Japan
Marlocks Technologies | EMEA
Shenzhen Fuwen Cloud | China
Starto Spark | APAC
twoday | EMEA

Amazon Connect Delivery Partners

AandT Systems | NAMER
Applogika | NAMER
CXBuilder.ai | NAMER

Amazon DynamoDB Delivery Partners

Aivar Innovations | APAC
Comprinno Technologies | APAC
Flexion | NAMER
KTM ONE | EMEA
Marlocks Technologies | EMEA

Amazon EC2 for Microsoft Windows Server Delivery Partners

Amazon ECS Delivery Partners

Ampity Infotech | APAC | Containers; Serverless Computing
CostQ | NAMER | Containers
Cygnet.One | APAC | Compute; Containers; Serverless Computing
Devoteam | EMEA | Compute; Containers; Serverless Computing
Necko Technologies | EMEA | Compute; Containers; Serverless Computing
Nusummit Technologies | APAC | Compute

Amazon EKS Delivery Partners

Atyos | EMEA
SQUAD | EMEA
XalDigital | LATAM
ZiMetrics | APAC

Amazon EMR Delivery Partners

AICorePro Technology | NAMER
ZiMetrics | APAC

Amazon Kinesis Delivery Partners

TemaBit | EMEA
Zero&One | EMEA

Amazon OpenSearch Delivery Partners

AICorePro Technology | NAMER | Log Analytics; Search
Business Compass | NAMER | Search
Cirrusgo | EMEA | Search

Amazon RDS Delivery Partners

知鱼智联科技股份有限公司 (CORECLOUD (HK) TECHNOLOGY) | China | Amazon RDS for MySQL
Aivar Innovations | APAC | Amazon RDS for PostgreSQL
Almoayyed Computers Middle East | EMEA | Amazon Aurora PostgreSQL
ASSURE IT | LATAM | Amazon RDS for MySQL
Bion Solutions | EMEA | Amazon Aurora PostgreSQL
CLOUD CHARIOTS | EMEA | Amazon RDS for MySQL
Cognitivo AI | LATAM | Amazon RDS for PostgreSQL
Enuaba Technologies | EMEA | Amazon Aurora MySQL; Amazon Aurora PostgreSQL
Frontier Business Systems | APAC | Amazon Aurora MySQL; Amazon RDS for MariaDB
NOVA Cloud | NAMER | Amazon Aurora MySQL
SK Inc. | APAC | Amazon Aurora PostgreSQL; Amazon RDS for PostgreSQL
Tuxpas | LATAM | Amazon Aurora PostgreSQL; Amazon RDS for PostgreSQL

Amazon Redshift Delivery Partners

Ancrew Global Services | APAC
DBiz Solutions | APAC
RYSUN LABS | NAMER
XalDigital | LATAM

AWS CloudFormation Delivery Partners

One Cloud Utopia | NAMER
ZirconTech | NAMER

AWS Config Delivery Partners

4STRATEGIES | LATAM
Aivar Innovations | APAC
dataRain Consulting | LATAM
Digico Solutions | EMEA
Kolomolo | EMEA
Necko Technologies | EMEA

AWS Control Tower Delivery Partners

AICorePro Technology | NAMER | Containers; Databases; Infrastructure as a Service; Security
Cloud Family AB | EMEA | Security
Enuaba Technologies | EMEA | Migration
Galactic Network | APAC | Databases; Migration; Security; Storage
Insight Enterprises | NAMER | Security
KTM ONE | EMEA | Security; Serverless
Lussiv | NAMER | Migration
Mayhive | EMEA | Migration
Searce | NAMER | Migration; Networking; Security

AWS Database Migration Service (DMS) Delivery Partners

Applify | NAMER
Clerion | EMEA
CloudifyOps | APAC

AWS Direct Connect (ISV and SI)

Atyos | EMEA
TemaBit | EMEA

AWS Glue Delivery Partners

Enuaba Technologies | EMEA
Hostin Services | APAC
RAMBUNCT CONSULTING | EMEA
RYSUN LABS | NAMER
The Black Puma | LATAM

AWS Graviton Delivery Partners

Aivar Innovations | APAC
HexaIO Kft. | EMEA
Maximyz Cloud | APAC

AWS IoT Core Delivery Partners

Yalantis | EMEA

AWS Service Catalog Delivery Partners

EVOLVENX | LATAM

AWS Systems Manager Delivery Partners

Aivar Innovations | APAC
dataRain Consulting | LATAM
Fixel Bilgi Iletisim Hiz AS | EMEA
KTM ONE | EMEA
Whistlemind Technologies | APAC

AWS Transfer Family Delivery Partners

Ampity Infotech | APAC
KTM ONE | EMEA

More Value, Greater Profitability for AWS Partners

Learn how AWS is transforming the partner experience >>

Transform CTEM with Zafran’s threat exposure management platform on AWS

Sriram Mohan — Fri, 06 Feb 2026 08:48:07 +0000

By: Len Gomes – AWS Partner Solutions Architect
By: Sriram Mohan – AWS Cloud Architect
By: Yonatan Zerahia – Zafran Director of Product
By: Nick Fisher – Zafran VP of Marketing

ZAFRAN

Vulnerability management has evolved beyond traditional scanning. Modern threat exposure management requires constant, AI-powered approaches that use Amazon Web Services (AWS) security services. Continuous threat exposure management (CTEM) represents a fundamental shift in how organizations address vulnerability management challenges across cloud, hybrid, and on-premises environments. Security teams struggle to manage vulnerabilities while sophisticated threat actors accelerate attack methods. Traditional vulnerability management with periodic scans and Common Vulnerability Scoring System (CVSS) based patching can’t keep pace with current attack patterns. Organizations need a new operating model that is continuous, contextual, and tightly aligned with business priorities.

This post explores how Zafran’s Threat Exposure Management Platform uses generative AI and AWS services to revolutionize vulnerability management through CTEM, so organizations can close the critical period between vulnerability discovery and risk reduction, referred to as the exposure window.

The vulnerability management challenge

As organizations adopt cloud innovations and expand their technology portfolios, their vulnerability to attack grows exponentially. Security teams struggle against both overwhelming volumes of findings and accelerating attack timelines, with threat actors exploiting vulnerabilities within only 5 days of publication and sometimes minutes when using AI tools.

Traditional vulnerability management approaches fail in this environment, creating a widening exposure window between discovery and remediation. Legacy scanners compound this problem through blind spots, fragmented data, and performance impacts across heterogeneous infrastructure. The most dangerous aspect is that attackers now preferentially target CVSS Medium-severity vulnerabilities over Critical and High-severity ones combined. This renders conventional severity-based prioritization ineffective, demanding a fundamental shift from periodic scanning to continuous, context-aware threat exposure management.

Introduced by Gartner in 2022, CTEM reframes vulnerability management through five stages: Scoping, Discovery, Prioritization, Validation, and Mobilization. The CTEM framework focuses finite security resources on exposures most likely to be exploited in specific environments. The following graphic illustrates the constant cycle of moving through these five stages.

Figure 1: Five stages of CTEM

	Traditional vulnerability management	CTEM
1	Periodic scans	Continuous discovery
2	Generic CVSS severity	Contextual exploitability with runtime analysis
3	Service level agreement (SLA)-driven patching	Risk-driven mitigation and remediation
4	Limited business context	Alignment with critical business services

CTEM helps chief information security officers (CISOs) improve mean time to remediate (MTTR), maintain audit readiness, and prevent breaches through context-aware, continuous operations.

Zafran’s solution: A CTEM engine for hybrid-cloud environments

Zafran’s Threat Exposure Management Platform operationalizes CTEM across AWS services, on premises, and for multi-cloud estates. The Zafran platform is a cloud-based architecture hosted on AWS. Zafran’s architecture uses AWS services for scalability, performance, and reliability.

The platform uses Amazon Bedrock for AI-powered remediation guidance, Amazon Simple Storage Service (Amazon S3) and AWS Lake Formation for scalable vulnerability data storage, AWS Lambda and Amazon EventBridge for real-time workflow orchestration, and Amazon API Gateway with AWS Identity and Access Management (IAM) for secure tool integration.

This architecture delivers elastic scalability, processing vulnerability data across hundreds of thousands of assets while maintaining subsecond query response times. For AWS customers, this means rapid onboarding through API-only, agentless integration and elastic processing for even the largest vulnerability datasets. The following diagram illustrates this architecture.

Figure 2: Zafran Threat Exposure Management Platform cloud-based AWS architecture

Unified exposure graph

Zafran processes signals from multiple security sources. These include vulnerability scanners, endpoint detection and response (EDR) tools, and cloud-based application protection platforms (CNAPP). The Zafran platform also integrates with firewalls and configuration management databases (CMDBs). Zafran creates a unified exposure graph that maps assets, common vulnerabilities and exposures (CVEs), MITRE ATT&CK techniques, and compensating controls, providing a single source of truth for vulnerability data.

Zafran integrates with your existing AWS security infrastructure through API connections, including:

Vulnerability management – Tenable, Qualys, Rapid7, Amazon Inspector

Cloud security (CNAPP) – Wiz, Prisma Cloud, Orca Security, AWS Security Hub

Endpoint protection (EDR) – CrowdStrike, Microsoft Defender, Trend Micro, SentinelOne

Network security – Palo Alto Networks, Fortinet, Cisco

Identity providers – Okta, Microsoft Entra ID (Azure AD), IAM

Ticketing and Security Orchestration, Automation, and Response (SOAR) – Jira, ServiceNow, Splunk

This agentless, API-based approach enables deployment in minutes without infrastructure changes or performance impact on production systems. Security analysts gain visibility into potential attack paths across the environment.

Contextual prioritization

Instead of relying solely on CVSS scores, Zafran evaluates exploitability using five environmental signals:

1. Runtime presence – Is the vulnerable component running?

2. Internet reachability – Is the asset exposed externally?

3. Active threat intelligence – Are attackers actively exploiting this CVE?

4. Control posture – Do web application firewall (WAF), next generation firewall (NGFW), or EDR policies block exploitation?

5. Business criticality – Does the asset support mission-critical workloads?

This contextual approach reduces false critical findings by 90%, so security and IT teams can focus resources on actual risk. The following screenshot shows the tool providing contextual risk scoring.

Figure 3: Contextual risk scoring reduces CVSS 9.2 Critical to 5.9 Medium

Proactive exposure hunting

Beyond reactive vulnerability management, security teams can use Zafran to proactively hunt for exposures before attackers exploit them. The platform answers critical questions that traditional scanners can’t:

Threat actor exposure – Am I exposed to RegreSSHion vulnerability or threats from APT29 or BlackBasta? Zafran surfaces high-profile vulnerabilities and reveals your specific exposure, enabling rapid deployment of fixes or mitigations.
Security control gaps – Am I exposed to gaps in CrowdStrike coverage? Zafran identifies gaps in security control coverage and exposures from vulnerable or outdated agents across your endpoint protection.
Identity misconfiguration – Am I exposed to identity attacks? Through integrations with identity providers such as Okta or Entra ID, Zafran discovers misconfiguration such as root users without multi-factor authentication (MFA).

These proactive hunting capabilities help security operations center (SOC) teams, threat intelligence teams, and incident response teams stay ahead of attackers by identifying and mitigating exposures before they become incidents.

Rapid risk mitigation

When patching requires extended change windows, Zafran prescribes fast mitigations through existing security controls, such as WAF rules, EDR policies, and CNAPP guardrails. This approach removes lengthy patch cycles from the critical path for immediate risk reduction. The following screenshot shows a CheckPoint Firewall policy change mitigating 20,000 vulnerabilities across 128 assets, demonstrating rapid risk reduction without patching.

Figure 4: CheckPoint Firewall

Agentic Remediation

CTEM’s fifth phase, Mobilization, represents where vulnerability management programs often stall. Tickets accumulate, ownership remains unclear, and MTTR increases. Zafran’s Agentic Remediation solves this challenge with AI-powered automation. Zafran’s generative AI engine, powered by Amazon Bedrock, consolidates overlapping CVEs into streamlined, high-fidelity tickets containing clear, step-by-step remediation actions. This eliminates redundant work and reduces noise for remediation teams. The following screenshot shows the UI for the remediation workflow with step-by-step patching.

Figure 5: Remediation workflow with step-by-step patching

Policy-driven assignment

Automate the assignment and delivery of remediation tickets to the appropriate responsible parties. Through administrator-defined rules, remediation tasks are automatically routed to the correct owners in platforms like Jira or ServiceNow, eliminating manual triage efforts and substantially reducing mean time to communicate (MTTC). This intelligent routing ensures faster response times and more efficient vulnerability management workflows.

Progress and SLA tracking

Zafran tracks MTTR, SLA deadlines, and residual risk in unified dashboards, providing CISOs with real-time evidence of program effectiveness for executive reporting and audit preparation. As patches deploy or mitigations activate, Zafran automatically re-scores exploitability and updates stakeholders, closing the loop between security and IT operations teams.

Real-world results

Organizations implementing Zafran’s CTEM solution across healthcare, manufacturing, and financial services report measurable security improvements:

A healthcare organization with over 50,000 employees and 200,000 assets reduced CVSS Critical vulnerabilities by 94% while measuring the efficacy and return on investment (ROI) of existing security tools
Hospital Sisters Health System (HSHS) reduced urgent vulnerabilities by 87% following implementation of contextual scoring and agentic remediation
Organizations report 90% decrease in false critical findings, 70% fewer remediation tickets through AI-powered deduplication, and workflow creation time reduced from hours to minutes

These results demonstrate measurable improvements in security posture while reducing operational burden on security and IT teams.

What sets Zafran apart?

Zafran delivers a unified Continuous Threat Exposure Management platform purpose-built for modern hybrid cloud environments:

Comprehensive exposure graph – Creates a unified view that maps assets, vulnerabilities, MITRE ATT&CK techniques, and compensating controls. Security teams can understand attack paths and exposure risks in context.
Context-driven prioritization – Reduces false critical findings by 90% by analyzing runtime behavior, network exposure, active threats, deployed controls, and business criticality.
Rapid risk mitigation – Uses existing security controls (such as CNAPP, EDR, and WAF) to prescribe immediate mitigations across thousands of assets, removing lengthy patch cycles from the critical path.
AI-powered remediation – Uses Amazon Bedrock to consolidate overlapping vulnerabilities into streamlined workflows with clear ownership, step-by-step guidance, and automatic SLA tracking.
Agentless deployment – Completes deployment in hours through API connections without infrastructure changes, agent deployment, or performance impact.

Built for AWS customers

Zafran’s architecture delivers specific advantages for organizations running workloads on AWS:

AWS integration offers:

Threat Exposure Management Platform integrates directly with AWS Security Hub for centralized security finding management
Amazon Inspector vulnerability data flows automatically into Zafran’s Exposure Graph
IAM identity analysis identifies misconfigurations like root users without MFA

Cloud-based scalability means that:

Elastic processing through AWS Lambda handles vulnerability datasets from tens to hundreds of thousands of assets
Amazon S3 and Lake Formation provide cost-effective, durable storage for historical exposure data
Multi-Region deployment options support global enterprise requirements

Threat Exposure Management Platform offers deployment flexibility and provides:

Availability through AWS Marketplace with simplified procurement and billing
Support for both multi-tenant SaaS and single-tenant deployment models
GovCloud availability for regulated industries and government agencies

For AWS customers extending security operations across hybrid environments, Zafran provides a unified platform that treats AWS findings (Amazon Inspector, AWS Security Hub, Amazon GuardDuty) alongside on-premises vulnerability data with equal depth and contextual analysis.

Conclusion

Modern vulnerability management demands active, contextual, and automated treatment of risk. CTEM provides the strategic framework; Zafran delivers the operational engine, powered by AWS services, enriched by existing security defenses, and accelerated by Agentic Remediation.

Whether you’re starting a CTEM program or scaling existing vulnerability management operations, Zafran provides the tools for you to:

Identify exactly which vulnerabilities are exploitable in your specific environment
Reduce exposure rapidly using security controls already deployed
Automate remediation workflows with AI-driven intelligence
Demonstrate measurable risk reduction to executives and auditors in real time

The combination of CTEM methodology, AWS Cloud services, and generative AI capabilities represents a fundamental shift in how organizations protect their expanding attack surfaces.

Ready to transform your vulnerability management program? Explore Zafran in AWS Marketplace or request a demo to see Agentic Remediation in action.

Zafran – AWS Partner Spotlight

Zafran is an ISV Partner with AI Software Competency, Zafran Threat Exposure Management Platform automatically maps security findings to controls already in your security stack, so that you know exactly what actions will provide the most risk posture improvement. With Zafran, you can significantly reduce the number of critical vulnerabilities, slash mean time to mitigate, and gain much-needed SLA relief. Contact Zafran | Partner Overview | AWS Marketplace

MoneySuperMarket’s AI leap with Firemind and AWS

Francisco Martins — Fri, 06 Feb 2026 08:09:08 +0000

By: Francisco Martins, Solutions Architect – AWS
By: Chris Owen, Chief Architect – MONY Group
By: Anna-Riitta Vuorenmaa, Chief Marketing Officer – Firemind

Firemind

MoneySuperMarket, AWS, and AWS Partner Firemind deployed an AI-powered assistant using Amazon Bedrock, moving from proof-of-concept (PoC) to production in just 6 months. While many organizations stall at the PoC stage when exploring generative AI, MONY Group plc (FTSE:MONY) and their brand MoneySuperMarket (MSM) took a different path, helping thousands of customers make smarter financial decisions faster with AWS.

Opportunity

MSM helps customers and households save money by simplifying search and comparison of deals across products such as home insurance, car insurance, travel insurance, credit cards, energy, and broadband.

They found that searching for the right credit card is often overwhelming, with customers having to evaluate introductory offers, understand if there’s an interest-free period, see if they meet the different eligibility criteria, and compare the product fees across different cards and providers. MSM decided to use AI to simplify this customer journey through the introduction of Money Concierge, an AI-powered assistant built on AWS.

Execution

To accelerate the path to production, MSM created a new team responsible for researching and testing AI technologies for their use cases, starting with a few members and recently expanding in size. The team was designed with the flexibility for experimentation, a strong sense of urgency, and with the autonomy to quickly identify and iterate on the PoCs. To accelerate the path to production, they chose Firemind as their AWS Partner, moving the Money Concierge solution from PoC to production within 6 months.

Solution architecture

MSM integrated Money Concierge into their credit card channel. When customers are evaluating different credit card offerings, they can select a widget that starts the Money Concierge experience. This assistant:

Understands the user’s financial position by extracting insights from completed forms and their credit report, such as their annual income, current indebtedness, credit balance needs.
Uses the third-party credit card providers’ APIs to get an updated overview of the available offers and terms.
Presents personalized suggestions in natural language, considers the user’s credit balance context and suggests a sample repayment plan customers can use as guidance to clear debt before interest applies. Figure 1 illustrates the Money Concierge experience with a sample repayment plan.

Figure 1: Money Concierge Experience and sample repayment plan

When MSM and Firemind decided to build Money Concierge with AWS services, they had a strong preference for fully managed or serverless services. This was to remove the administrative burden of managing servers, allowing product teams to focus on building features that sustain a strong customer experience.

Amazon Simple Storage Service (Amazon S3) is the main storage service for documentation and terms and conditions (T&C) of the different credit card offerings. It also hosts the static website as the origin for the content delivery network (CDN).
AWS Lambda is the main serverless compute service used to run code without managing servers. It’s used first as an API endpoint to receive customer requests and starts an AWS Step Functions workflow. Lambda functions attach to the private subnets of MSM’s VPC, with no access to the internet, allowing the functions to access private API endpoints and securely retrieve data from credit reports and offers.
AWS Step Functions orchestrates the different steps required to build the customer credit profile. It coordinates the different backend workflows that collect and enrich customer data, while tracking the workflow state across steps. Finally, it generates the custom prompts enriched with customer context that are going to be sent to Amazon Bedrock for the final customer output.
Amazon Bedrock provides secure and scalable generative AI model access on-demand. VPC-attached Lambda functions connect privately to Amazon Bedrock through an AWS PrivateLink VPC endpoint, so that data doesn’t leave AWS and is kept under MSM’s control. Another key advantage of Amazon Bedrock is that data isn’t used to improve the underlying large language model (LLM) performance, adding another layer of data privacy in the context of responsible AI. Requests to the Amazon Bedrock API use a secure (TLS) connection, which encrypts data in transit. Amazon Bedrock Knowledge Bases embed key MSM documentation and T&C, delivering accurate context-aware generation through Retrieval-Augmented Generation (RAG). This feature uses Amazon OpenSearch Serverless as the underlying vector store.

Figure 2: AWS Architecture supporting Money Concierge

This solution uses a highly available and scalable modular architecture. Figure 2 illustrates the AWS Architecture and services supporting the Money Concierge solution. MSM deployed the workload across different Availability Zones within the selected AWS Region, and the underlying services automatically adjust capacity based on the actual workload demand. By designing for modularity, MSM can also extend the solution to additional financial products in the future.

Results

MSM’s Money Concierge has helped over 55,000 customers in their credit card journey. MSM’s customers receive:

Faster access to relevant card options based on eligibility.
Clearer explanations of card benefits and limitations.
Greater confidence in their decision-making, leading to improved conversion rates.

Money Concierge provides improved customer engagement and conversion and received 93% positive feedback.

Conclusion

This post illustrates how AWS and partners like Firemind can help organizations accelerate their ambitions to deliver customer-facing AI solutions into production. The key items to drive value are:

Start with the customer; identify their pain points and work backwards to build an experience that addresses that need.
Rethink your organization structure by having a dedicated team responsible for researching and experimenting new technologies.
Work with AWS Partners to bring the best ideas to production.

If your organization is yet to drive value from AI, consider these next steps:

Review the AWS AI/ML Solutions page.
Explore Amazon Bedrock and Amazon’s approach to responsible AI.
Learn more about MONY Group’s Data Strategy for AI in financial services.
Contact your AWS Account Manager to discuss AI partnership options. Bring insights on your biggest challenges, ambitions, and timelines on moving AI to production and start your move to AI solutions.

Firemind – AWS Partner Spotlight

Firemind is an AWS Premier Tier Consulting Partner that provides end-to-end solutions that help businesses harness data, enhance decision-making, and drive measurable outcomes. With deep expertise in Machine Learning, AI, and AWS Cloud Engineering, they accelerate AI adoption through solutions like Pulse – generative AI accelerator – and Managed AI – AI workload management tool.

Contact Firemind | Partner Overview | AWS Marketplace

Generative AI for operations leaders: How Domo and Amazon Bedrock optimize efficiency

Tamara Astakhova — Wed, 04 Feb 2026 21:00:18 +0000

By: Cody Irwin, AI Adoption Director – Domo
By: Tamara Astakhova, Sr. Partner Solutions Architect – AWS
By: Aman Tiwari, Solutions Architect – AWS

Domo

Operations leaders, such as chief operating officers (COOs), need to address growing data volumes and rising performance expectations while still relying on outdated manual processes and static reporting tools. This disconnect creates significant operational inefficiencies and delays critical decision-making. Companies face increasing pressure to improve operational efficiency and maintain competitive advantages in their industries, making responding to this trend particularly urgent. To address these needs, the combination of Domo’s agentic AI and Amazon Bedrock offers a practical way to modernize operations, improve decision-making, and deliver measurable business results.

Domo is an AWS Advanced Tier Technology Partner, AWS ISV Accelerate Partner, and AWS Marketplace Seller with Data & Analytics and Machine Learning competencies. Domo Data Platform streamlines data management using integrations, transformation tools, and AI-powered visualization capabilities in a comprehensive solution.

In this blog post, we explore how generative AI transforms operations and how integration of Domo’s agentic AI with Amazon Bedrock helps COOs achieve greater efficiency, automation, and operational performance.

How agentic AI can transform your operations

One of the most exciting advancements brought by generative AI is the move from tools that only describe data to tools that actively do something with it. Domo’s agentic AI, powered by Amazon Bedrock, transforms your operational workflows by tackling some of the most time-consuming and complex tasks you manage today.

Amazon Bedrock is a comprehensive, secure, and flexible service for building generative AI applications and agents. It provides access to leading foundation models, agent deployment services, and tools for model optimization and data integration, connecting applications with current business data.

You no longer need to settle for charts or tables that must be painstakingly interpreted. AI provides context and narratives that help you understand why a metric matters and what you should do about it. This elevates reporting from informational to actionable, saving hours of manual work and enabling faster decision-making. This AI-driven approach to operational intelligence enhances understanding and unlocks new efficiencies across various aspects of business operations. The key areas where this impact is particularly significant are:

Resource allocation simplified
Imagine having an intern capable of optimizing how resources are allocated. Whether adjusting staffing needs based on sales forecasts or determining where excess inventory can be most effectively utilized, Domo’s AI agents analyze trends, predict outcomes, and recommend actions that save time and reduce waste.
Automating repetitive tasks
COOs often oversee workflows riddled with repetitive tasks, such as processing thousands of support tickets, reviewing supply chain bottlenecks, or aggregating vendor data. With agentic AI, these tasks become automated. For example, AI can surface the most pressing issues in your supply chain and pre-sort solutions for your team to act on.
Operational performance optimization
Operations run on metrics—but making sense of them at scale can be overwhelming. AI doesn’t only visualize data; it intelligently communicates what the data means and what actions you need to take. For example, AI can identify regions with declining performance, highlight root causes (for example, staffing shortages and seasonal trends) and recommend options to improve outcomes.
Scenario planning for better decisions
What if you could simulate operational scenarios before making changes? Domo’s AI agents enable this as part of planning. For example, you can forecast how shifting procurement strategies will affect profit margins or how increasing production will impact delivery times and warehouse needs.

The ultimate outcome of AI is transforming the COO role from reactive problem-solving to proactive value creation. That transformation happens by turning data into narratives and automating workflows, so you can make more impactful decisions faster.

To illustrate these benefits let’s consider the following real business scenarios:

An AI agent uncovers bottlenecks in assembly processes, pinpointing recurring delays caused by machine downtime. By recommending predictive maintenance and labor reallocation, it helps reduce cycle times by 20% and boosts overall productivity.
AI analyzes inventory trends and forecasts seasonal demand with accuracy. It helps optimize stock levels; minimizing shortages and overages while improving cash flow and helping to ensure that products are available when customers need them.
AI enhances fraud detection by identifying suspicious patterns during transaction processing in real time. This speeds up detection, improves compliance, and strengthens security measures to protect both businesses and customers.

Teams can use AI agents to operate with greater efficiency, precision, and speed, turning complex challenges into streamlined, outcome-driven solutions. Whether it’s on the production floor or at the back end of financial operations, AI is shaping the future of operational excellence.

Why AWS and Amazon Bedrock

The decision to design and build Domo’s AI capabilities on AWS and Amazon Bedrock was strategic, supported by four key factors:

Seamless infrastructure for operational reliability

For COOs, reliability is everything. AWS aligns perfectly with Domo’s existing infrastructure, enabling smooth scaling without disruptions to performance. This helped ensure that Domo AI could handle everything from lightweight reporting workflows to complex automation tasks without skipping a beat.

Uncompromising security

Operational leaders must carefully manage confidential data, including employee personal information, customer financial records, and business intelligence. Human resources schedules, supplier contracts, and financial forecasts require secure interactions.
AWS security guardrails integration: Because Domo operates as a cloud-based software as a service (SaaS) platform running on AWS infrastructure, the integration with Amazon Bedrock creates a secure, private cloud environment where customer data never leaves the AWS ecosystem. This architecture addresses critical data residency requirements while maintaining the highest security standards.

Key security benefits:

Data isolation: With Amazon Bedrock, Domo helps ensure that data remains within the AWS hosting environment, helping prevent model providers from accessing or training on customer data
No data logging: AWS does not log or store customer prompts and completions, helping to ensure privacy of business interactions
Encryption: With encryption in transit and at rest, along with restricted access to model deployment accounts, Amazon Bedrock provides robust data protection
Access controls: Comprehensive identity and access management through AWS Identity and Access Management (IAM) helps ensure that only authorized personnel can access sensitive systems

Responsible AI and compliance framework: Domo’s integration with Amazon Bedrock provides enterprise-grade security and compliance that operations leaders require. Customer data never leaves the AWS ecosystem, with strict data isolation helping to ensure that model providers cannot access or train on proprietary information. Domo maintains compliance with critical standards including GDPR, HIPAA, SOC 2 Type II, and ISO 27001, while their Responsible AI program aligns with OWASP guidelines for large language models (LLMs). This comprehensive security framework, combined with the AWS Shared Responsibility Model, enables organizations to confidently use AI-powered insights while meeting their regulatory obligations and maintaining data sovereignty.

Outstanding AI models for business applications

Amazon Bedrock provides access to the most robust language models, including Anthropic Claude, which power Domo’s agentic AI. This helps ensure that every data-driven recommendation or action taken by the AI is crafted with the precision and relevance that operations demand.

Scalability for growing businesses

With AWS, Domo’s solution delivers consistent high performance and reliability, scaling effortlessly to meet the demands of organizations from small startups to global enterprises.

DOMO AI architecture

Domo’s AI architecture, shown in Figure 1, represents a sophisticated end-to-end solution that transforms raw AI capabilities into business-ready applications.

Figure 1: Domo’s AI architecture

This architecture shows a progression from foundational AI capabilities through tools and integration layers to end-user experiences. Domo’s AI integration system can be broken down into four main components:

AI Foundation layer:
- Advanced LLMs and machine learning (ML) models, featuring DomoGPT powered by Anthropic models through integration with Amazon Bedrock
- Comprehensive AI model management capabilities
- Enterprise-grade AI governance to help ensure secure and controlled AI operations
Tools & Knowledge is the intelligence layer transforms raw AI capabilities into practical tools:
- AI services for text, image, and data processing
- Custom ML models for specialized business needs
- Domo Universal Models support common analytical tasks such as forecasting, sentiment analysis, outliers, and PII detection
- A powerful semantic layer that contextualizes data
- Sophisticated data management through datasets and file sets, using Amazon Bedrock Knowledge Bases
- Memory systems for maintaining context and user interactions
AI Integration is the orchestration layer brings intelligence to life through:
- The agentic framework with pre-built and custom agents for specific business needs
- Direct endpoints for seamless ML model integration
Business Consumer Experience is the final layer delivers AI capabilities to end-users through:
- Intuitive Domo AI Chat and custom chat assistants (for more information, see Exploring data using AI chat at Domo with Amazon Bedrock)
- Purpose-built feature assistants
- Automated workflows
- Interactive Jupyter Workspaces for advanced users

Customer Story

As an example of the transformative potential of Domo’s AI capabilities on Amazon Bedrock, Opus Inspection, the largest vehicle inspection provider in the U.S., partnered with Domo and AWS to modernize its fragmented data systems and streamline vehicle safety and emissions testing across jurisdictions. By consolidating data into a unified, real-time platform, Opus reduced software maintenance efforts by up to 50%, accelerated customer deliveries, and laid the groundwork for AI-driven innovation. With Domo powering dashboards and serving as the front end for AWS integrations, Opus created a seamless user experience, enabling government agencies and repair centers to track performance and uncover trends efficiently. This transformation positions Opus to enhance safety, reduce emissions, and drive future advancements in AI workflows. Visit the Domo website to learn more about the Opus Inspection customer story.

Conclusion

If you’re a COO, the question isn’t whether generative AI has a role in your operations. The question is how you will use it to lead. With Domo and Amazon Bedrock, you have the tools to reimagine operational efficiency, enhance your workflows, and drive measurable business outcomes. The decision to adopt AI in operations isn’t only about keeping up with technology. It’s about amplifying your ability to lead with efficiency, intelligence, and confidence. With Domo and Amazon Bedrock, the future of operational excellence starts now. To bring AI to your operations, start exploring how Domo and Amazon Bedrock can transform your processes and outcomes with a free demo today.

Visit the Domo website to learn more about the Domo agentic AI solution, which is available for purchase through AWS Marketplace. For more information about the solution or to see a demonstration and start a free trial, contact Domo.

Domo – AWS Partner Spotlight

Domo is an AWS Machine Learning Competency Partner and modern, cloud-based business management platform that puts real-time data directly in the hands of everyone in the organization.

Contact Domo | Partner Overview | AWS Marketplace

Behind Securonix’s Unified Defense SIEM Spark Architecture on Amazon EKS: 99.99% Availability Across 10,000+ Concurrent Jobs

Scott Sundby — Tue, 03 Feb 2026 20:04:22 +0000

By: Sanjeev Kishore Yarnapati, Director of Infrastructure Engineering – Securonix
By: Scott Sundby, Senior Technical Account Manager – AWS

Securonix

When protecting over 300 global enterprises from cybersecurity threats and processing more than 78 million security events per second, system performance isn’t just a benchmark, it’s mission critical.

Securonix, a six-time Gartner® Magic Quadrant Leader for Security Information and Event Management (SIEM), delivers AI-powered security analytics and operations at enterprise scale. The system combines SIEM, User and Entity Behavior Analytics (UEBA), and Security Orchestration, Automation and Response (SOAR) and Threat Intelligence capabilities to help organizations reduce cybersecurity threats in near real time.

As Securonix continued its rapid growth, its architecture—built on HBase and Amazon EMR running on Amazon Elastic Compute Cloud (Amazon EC2)—had supported expanding workloads for years. With rising data volumes and an increasingly complex threat landscape, the company saw an opportunity to modernize its infrastructure to deliver even greater scalability, resilience, and operational simplicity. A routine Amazon EMR upgrade in early 2024 surfaced areas where system reliability could be further enhanced, prompting a proactive architectural review to support future performance and growth.

Recognizing the need for a strategic transformation, Securonix turned to AWS Enterprise Support to collaborate on this modernization journey. Together, they worked to create a foundation that could simplify operations, strengthen availability, and help Securonix continue to scale to meet growing customer demand.

“High availability and scalability are fundamental to our platform as demand grows. AWS Enterprise Support worked hand in hand with us to align technical outcomes with our business objectives, helping us capitalize on growth opportunities.”

– Sanjeev Kishore Yarnapati, Director of Infrastructure at Securonix

Modernizing Unified Defense SIEM Spark Workloads with Kubernetes at Scale

After conducting technical deep dives with AWS, Securonix launched a 14-month project to modernize its Spark processing environment using Amazon Elastic Kubernetes Service (Amazon EKS). In addition to AWS Enterprise Support, Securonix also worked closely with the Data on Amazon EKS (DoEKS) team to design a Kubernetes-based architecture optimized for scale, cost-efficiency, and resilience. The migration was intentionally phased, beginning with lower-volume workloads to validate performance, and progressively expanding to support the company’s highest-volume, most complex, and performance-critical workloads.

The resulting system runs on more than 40+ Amazon EKS clusters, each supporting between 20−50 customers. These clusters collectively execute over 10,000 concurrent Spark jobs across 5,000–6,000 Amazon EC2 instances. Key components of the architecture include:

• AWS Graviton for cost-efficient compute
• Karpenter for dynamic node provisioning
• Amazon EC2 Spot Instances for fault-tolerant workloads
• On-Demand Instances for high-SLA processing
• Kubeflow Spark Operator to manage Spark job lifecycles

Security data flows from customer environments through custom publishers to Apache Kafka, and then into Spark for real-time analysis on Amazon EKS. With Karpenter provisioning new Amazon EC2 nodes in seconds, the system scales based on demand while maintaining high availability.

A Collaborative Build with AWS Enterprise Support

Throughout the Spark migration, AWS Enterprise Support played a hands-on role in guiding architecture decisions and solving complex technical challenges. AWS Technical Account Manager (TAM) worked closely with Securonix engineers to whiteboard solutions, evaluate proofs of concept, and run deep dives on orchestration, scaling, and cost attribution.

To support the project, the TAM also brought in AWS specialists. Together, the team helped Securonix implement and tune critical components like AWS Graviton and Karpenter.

“Throughout the project, the AWS team helped us think through everything from scaling strategies with Karpenter to cost attribution and performance tuning with AWS Graviton. Their guidance was detailed and practical—they became a part of our team weighing architecture and implementation trade-offs to give the best experience for our platform and customers.”

– Sanjeev Kishore Yarnapati, Director of Infrastructure at Securonix

Conclusion

Securonix’s modernization effort delivered an approximate of 30% improvement in cost performance. This was driven by the price-performance advantage of AWS Graviton and dynamic node provisioning using Karpenter, with Karpenter contributing to a 3–5 percent improvement in operating efficiency. Other improvements across cost, performance, and operations include:

• 99.99% availability maintained across Spark processing workloads
• 91% faster code deployment, reduced from 3–4 hours to 15 minutes
• 50% reduction in job recovery time following Spark job failures
• Hundreds of enterprise customers migrated with minimal disruption
Freed engineering teams from EMR maintenance, improving velocity and enabling focus on strategic initiatives

The migration also had a positive downstream effect on workloads that remained on Amazon EMR, which benefited from reduced processing load and improved stability. With Spark jobs migrated to Amazon EKS, the load on the legacy environment dropped significantly—creating a more stable upgrade path and reducing the risk of future service disruption. The modernization also laid the groundwork for future product expansion, giving Securonix a reusable architectural model for additional services.
Securonix plans to expand its use of Amazon EKS (Elastic Kubernetes Service) by adding more services to the system. The company is also investigating how to use Amazon Bedrock’s generative AI capabilities with enhanced security features and investigation tools.

“Amazon EKS has greatly simplified our deployment process. “We’re actively searching for additional ways to enhance our methods and expand our capabilities.”

– Sanjeev Kishore Yarnapati, Director of Infrastructure at Securonix

Securonix – AWS Partner Spotlight

Securonix is an AWS Advanced Technology Partner that provides cybersecurity with a Unified Defense SIEM powered by AWS and agentic AI, purpose-built to decide and act across the threat lifecycle.

Contact Securonix | Partner Overview | AWS Marketplace

Scale AI application in production: Build a fault-tolerant AI gateway with SnapSoft

Lu Zou — Mon, 02 Feb 2026 23:01:46 +0000

By: Gergely Szlobodnyik, Head of AI & ML – SnapSoft
By: Lu Zou, Sr. WW Partner Solutions Architect – AWS

SnapSoft

Production AI applications face unique scaling challenges. When you build generative AI applications, you integrate multiple AI models from various providers. Each provider sets model-specific quotas and regional endpoints. Under unpredictable workloads, your architecture must handle thousands of concurrent requests without service disruptions.This post introduces an AI gateway solution. The AI gateway creates a resilient, fault-tolerant architecture by routing requests across multiple providers, accounts, and AWS Regions. You can scale confidently without hitting quota limits or experiencing provider outages.

You’ll learn the architecture at multiple layers:

- Logical architecture – Understand how the gateway routes requests to AI endpoints
- Amazon Web Services (AWS) solution architecture – Configure auto scaling, load balancing, health checks, and dynamic failover
- Real-world implementation – Learn how one company migrated to production successfully

Common Challenges in Scaling AI Applications in Production

Single-provider architectures create critical risks. Most organizations start with one AI provider and endpoint. This approach works for prototypes but fails at scale. Consider these common challenges:

- - Provider dependency – A single endpoint can’t guarantee service when workloads scale unpredictably. Provider outages leave your entire application unavailable. You have no fallback when your primary provider experiences issues.
  - Quota limitations – Each provider sets account-level or subscription-level quotas. When you reach these limits, the provider throttles or rejects requests. This causes immediate service outages. Many providers offer soft limits that you can request to increase. However, quota increases require manual approval and can take days or weeks. You can’t increase quotas automatically when traffic spikes.
  - Noisy neighbor problem – When multiple applications share the same AI endpoint, they compete for resources. One application’s traffic spike affects all other applications. You can’t isolate workloads or guarantee capacity for critical applications.
  - Manual failover – When endpoints fail or quotas are exceeded, you must manually switch to backup providers. This requires operator intervention and causes extended downtime. You can’t respond quickly to production incidents.
  - Regional limitations – Providers offer models in specific regions. Regional outages or high latency affect your application performance. You can’t automatically route to the best-performing endpoint.

These challenges require a new architectural approach. The AI gateway addresses each challenge through multi-provider routing, automatic failover, and distributed quota management.

SnapSoft’s AI Gateway Logical Architecture

The AI gateway uses a multistep routing mechanism. The gateway selects the best model provider, AI model, account, and regional endpoint for each request. This dynamic routing means your requests reach available endpoints, even when quotas are exceeded or endpoints fail.

Figure 1 shows the routing mechanism. The gateway routes requests through four selection steps:

- 1. Provider selection – You can integrate multiple model providers, including cloud and on-premises solutions. The provider selector automatically routes requests to providers with available endpoints.
  2. Model selection – After you choose a provider, you configure which model to use. The gateway routes requests to that model’s endpoint instances. You can reconfigure models in real time. The provider selector skips providers without available endpoints for your configured model.
  3. Account selection – The account selector tracks quota usage and consumption for each model. When an account reaches its quota limit, the selector fails over to other accounts with equivalent endpoints. This provides high availability. For example, an AWS account tracks quota status, including request count and tokens. In Amazon Bedrock, model quotas apply per account across all geographic AWS Regions where the model is available.
  4. Regional endpoint selection – The Region selector routes requests to specific endpoint instances. You can choose Regional endpoints with the best latency and throughput characteristics.

Figure 1: AI gateway routing mechanism

SnapSoft’s AI Gateway AWS Solution Architecture

The AI gateway uses a hub-and-spoke architecture. Each hub connects to a specific model provider: Amazon Bedrock, third-party APIs, or on-premises models. All hubs share a single entry point and DNS routing mechanism.

Amazon API Gateway serves as the single entry point. You can configure authentication, authorization, and rate limits through usage plans. An AWS Lambda function acts as the gateway proxy and routes requests. The function uses provisioned concurrency for high availability and connects to a virtual private cloud (VPC) for secure network access. The function can also filter content before forwarding requests.

A private hosted zone in Amazon Route 53 enables dynamic routing. The zone contains weighted alias records that point to Network Load Balancer endpoints for each provider hub. The Lambda function queries Amazon Route 53 resolver for available endpoints. The resolver returns only healthy Network Load Balancer IP addresses. Health checks run every 10 seconds to provide fast failover. The weighted alias records distribute requests uniformly across all healthy hubs with low time to live (TTL) (10 seconds) for fast DNS convergence.

Each provider has a dedicated hub-and-spoke module. The hub performs load balancing, health checking, and automatic failover. Spokes represent independent AWS accounts with separate quota limits. Each hub uses a Network Load Balancer that routes traffic to spoke accounts through AWS Transit Gateway. The Network Load Balancer performs health checks on spoke endpoints using the /healthz endpoint.

Each spoke contains a Network Load Balancer with static IP addresses and an AWS Fargate service. The Fargate service hosts the containerized application that forwards requests to AI endpoints. The service auto scales based on incoming request volume. The application provides two endpoints: /healthz for health checks and /inference for forwarding requests. The application includes backoff strategies, Regional failover, and model failover capabilities.

The architecture supports three provider types:

- - Amazon Bedrock (recommended) – Amazon Bedrock provides more than 100 on-demand models from Anthropic, Meta, Cohere, Mistral AI, and others. Bedrock uses the AWS backbone network for low latency and offers cross-Region inference for automatic Regional failover. The hub-and-spoke architecture distributes load across multiple AWS accounts to overcome account-level quota limits. We recommend Amazon Bedrock as your primary provider for production workloads.
  - Third-party APIs – You can integrate third-party providers such as OpenAI, Anthropic, and DeepSeek. Store API keys in AWS Secrets Manager for secure access. Third-party integration requires internet connectivity and might have higher latency compared to Amazon Bedrock. Use third-party APIs to supplement Amazon Bedrock when you need access to specific models.
  - On-premises models – You can connect self-hosted models in your data center through VPN connections with SSL/TLS encryption. Transit Gateway serves as the AWS VPN endpoint. On-premises integration enables cost-efficient hosting of smaller, high-performing models. This option works well when you have existing infrastructure or need full control over model deployment.

Figure 2: AI gateway architecture

Although this diagram focuses on core components, a production deployment should include additional security controls. Implement authentication using Amazon Cognito at the API gateway, enforce TLS encryption across all communications, and apply rate limiting with AWS WAF to prevent abuse. Store third-party API keys in AWS Secrets Manager. For AI-specific security controls such as content filtering and prompt injection prevention, implement Amazon Bedrock Guardrails. Finally, enable Amazon CloudWatch and AWS CloudTrail for monitoring and incident detection.

Case Study

GDE-MIT builds an EdTech platform that helps thousands of students learn through AI-powered chat. To use the platform, schools upload documents to create knowledge bases and students ask questions about subjects, lectures, and books. The platform ran on Microsoft Azure with a single OpenAI endpoint. This architecture created critical risks. When OpenAI experienced outages, the entire service went down. The single endpoint couldn’t handle production-scale traffic, and quota limits caused frequent throttling. GDE-MIT needed a resilient, scalable solution before expanding to more schools.

SnapSoft assessed the architecture and identified critical gaps by mapping GDE-MIT’s IT landscape and analyzing the data and AI architecture. The assessment identified inefficiencies in business continuity, fault tolerance, latency, and cost. SnapSoft recommended migrating to AWS and implementing the AI gateway solution to address these gaps.

The AI gateway eliminated single points of failure. The gateway routes requests across Amazon Bedrock, third-party APIs, and on-premises models. When one provider fails or reaches quota limits, traffic automatically fails over to available endpoints. The solution distributes load across multiple AWS accounts with independent quotas. The platform now achieves:

High availability – Significantly reduces service interruptions from provider outages
Substantial cost reduction – Amazon Nova models offer substantial cost savings compared to GPT-4.5
Significant throughput improvement – Load balancing across multiple endpoints can increase aggregate tokens per minute
Improved latency – AWS backbone network reduces time to first token compared to internet-facing communication
Enhanced security – Reduced attack surface through private AWS network instead of public internet
Production scale – Handles hundreds of thousands of requests per day

The AI gateway allows GDE-MIT to scale confidently while maintaining continual availability and controlling costs.

Conclusion

The AI gateway solves the critical challenges of production AI applications. The multi-provider architecture eliminates single points of failure by routing requests across providers, accounts, and Regions. When quota limits are reached or endpoints fail, automatic failover occurs in seconds. Health checks run continually to detect and route around unhealthy endpoints. You can combine the Amazon Bedrock low-latency backbone network with third-party APIs and cost-efficient on-premises models.

Key benefits:

Automatic failover – Routes to healthy endpoints when quota limits are reached or providers fail
Multi-provider support – Integrates Amazon Bedrock, third-party APIs, and on-premises models
Fast recovery – Detects and routes around unhealthy endpoints in seconds
Quota management – Distributes load across multiple accounts to prevent exhaustion
Noisy neighbor prevention – Isolates applications through account-level separation
Cost optimization – Balances expensive third-party APIs with cost-efficient on-premises models
Minimize manual intervention – Scales automatically without operator involvement

You can scale to serve millions of users without manual quota management or service disruptions. The architecture delivers the high availability and resilience your production applications require. To learn more about implementing the AI gateway architecture described in this post, contact the SnapSoft team. Their experts can help you assess your current AI architecture and design a fault-tolerant gateway tailored to your production requirements.

New to AWS? Become an AWS Partner to build, market, grow and scale your business.

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SnapSoft – AWS Partner Spotlight

SnapSoft is an AWS Premier Tier and AWS Competency Partner that excels in cloud migrations, GenAI, DevOps, and application development, enabling startups, SMBs, and enterprises to seamlessly deploy AI, transition to AWS, and improve DevOps speed while driving innovation, reducing costs, and enhancing security.

Contact SnapSoft | Partner Overview | AWS Marketplace

Unlock test intelligence with natural language: QMetry and Amazon Bedrock integration

Carl Luo — Wed, 28 Jan 2026 18:13:51 +0000

SmartBear

By: Matt Bonner, Solutions Architect – SmartBear
By: Carl Luo, Partner Solutions Architect – AWS
By: Randy Weinstein, Sr. Solutions Architect – AWS
By: Sophia Folino, Associate Solutions Architect – AWS

Software deployment is moving from weekly to daily cycles as organizations adopt continuous integration and continuous delivery (CI/CD) techniques. With this shift, quality assurance has become central to maintaining velocity without compromising reliability. These changes have raised new challenges for trying to turn test data into actionable insight.

QMetry from SmartBear powered by Amazon Bedrock transforms how teams plan, manage, and analyze testing. In this post, you’ll learn how SmartBear AI uses Amazon Bedrock to transform natural language questions into SQL queries, delivering instant insights from your test data.

The challenge: Transforming test data into strategic insights

Across industries, software quality has become a core business driver that shapes customer experience and brand reputation. Executives and product leaders depend on accurate testing insights to make release decisions, manage risk, and maintain confidence in delivery.

According to Capgemini World Quality Report 2025-26, automation is at a crossroads with nearly 50% of organization still in the planning stage and coverage averaging only a third of test cases. That gap shows the ongoing challenge of turning test data into actionable intelligence that supports confident release decisions. Test results are often stored across multiple systems, making it difficult to understand the overall state of product readiness. Reports take time to generate, and data will lose relevance before it reaches the people who need it most.

When quality data is hard to access, the difficulty in generating insights limits visibility at critical stages of release planning, increasing the uncertainty organizations face, which can delay innovation and increase risk.

The opportunity is clear. Teams need a testing solution that moves beyond collecting data to interpreting it in real time, providing leadership with the clarity and confidence to release software at the pace of the business. QMetry uses Amazon Bedrock to combine the power of AI with modern quality management to make testing a source of strategic advantage.

Rethinking test management with AI

Traditional test management often limits how quickly teams can uncover insights. Fortune 500 firms typically rely on manual reporting, complex query building, and multiple layers of data navigation before meaningful information becomes available. These steps slow down collaboration and create unnecessary dependence on technical resources.

With SmartBear AI powered by Amazon Bedrock, teams can remove those barriers. The platform introduces smart AI search and AI-powered SQL generation. These features mean that users can interact with test data using straightforward, natural language.

Figure 1: SmartBear AI SQL generator

Instead of learning SQL query syntax or waiting for analytics support, quality assurance (QA) leaders can ask questions like, “Which test areas have the highest defect density?” or “How ready is this release?” and receive aggregate data from all integrated systems within seconds. This transforms how teams explore information, eliminating repetitive tasks and making insights accessible to everyone.

Figure 2: Custom QMetry reports and tables generated from QI

By turning natural language into real-time analytics, QMetry helps teams find information faster, shortens feedback loops, and makes confident release decisions. The result is a more intelligent and responsive approach to quality management that aligns perfectly with the pace of modern software delivery.

Accelerating insights with Amazon Bedrock

Amazon Bedrock is a fully managed service that organizations can use to build and scale generative AI applications using foundation models (FMs) from trusted providers. For QMetry, this AI platform integration makes it possible to deliver new AI capabilities quickly and securely while avoiding the complexity of maintaining model infrastructure.

Model access, governance, and data protection features available in Amazon Bedrock allow QMetry to focus on delivering intelligent quality management insights to your team. The platform uses AI to understand the connections between test cases, requirements, and quality metrics, turning large volumes of information into practical insights that guide better decisions.

Built on the foundation of Amazon Bedrock, QMetry takes advantage of AWS proven infrastructure for security, reliability, and compliance, and customers reap the benefits from this continued innovation in AI. QMetry has implemented robust security practices aligned with the AWS shared responsibility model. Together, QMetry and Amazon Bedrock create a modern testing environment where data moves securely, insights appear instantly, and organizations can release software with confidence.

Customer success

One of QMetry’s marquee customers is a Fortune 500 financial services firm with a vast portfolio of digital products, each subject to rigorous regulatory and performance standards. The company struggled to unify test data across multiple teams, applications, and development streams. Reports on coverage, defects, and release of readiness were compiled manually, with heavy reliance on SQL queries and cross-team coordination. By the time analytics reached leadership, insights were often stale and lacked the context needed for confident decisions.

Upon adopting QMetry’s platform, the customer began integrating test metrics, requirements, and defect tracking under a centralized system. With the addition of SmartBear AI built on Amazon Bedrock, the firm gained a new capability: QA leaders and stakeholders could now use natural language to interrogate their test data. Questions such as “Which modules have the most open critical defects?” or “Do we have tests covering the highest risk features in this release?” return instant, actionable reports.

The transformation yielded immediate measurable benefits:

The time required to generate reports shrank from hours to seconds.
Visibility into coverage and defect trends improved, giving leadership real-time decision support.
The company accelerated its release cadence, with greater confidence in quality outcomes.
Cross-functional teams aligned more easily, using shared insight rather than fragmented metrics

By turning testing data into accessible intelligence, this enterprise strengthened its ability to deliver mission-critical software while meeting compliance requirements, improving customer trust, and reducing time to market.

Together, QMetry and AWS empower organizations to achieve continuous quality faster and more efficiently. By using Amazon Bedrock, QMetry brings the power of generative AI directly to quality management, giving teams faster access to insights that require time, coordination, and technical expertise. Customers benefit from real-time visibility in testing progress, better alignment between QA and development, and greater confidence in release decisions. With AI simplifying data exploration and reporting, teams can focus on delivering innovation rather than managing complexity.

This combination empowers enterprises to accelerate delivery without sacrificing quality, turning testing into a strategic advantage that supports faster growth, improved customer satisfaction, and stronger business outcomes.

Looking ahead

AI is transforming how quality assurance contributes to business success. What at one time was a reactive step in the delivery process is now becoming a continuous source of insight that drives better decisions and faster innovation. With capabilities such as smart AI search and AI-powered SQL generation, QMetry gives teams the tools to shift their focus from managing data to improving outcomes.

As organizations continue to modernize their testing ecosystems, AI will play a central role in shaping how quality is measured, managed, and optimized. By using the Amazon Bedrock fully managed generative AI service, QMetry is helping enterprises move toward an era of intelligent, connected quality management where every decision is informed by data and every release builds trust with customers. The future of testing is proactive, adaptive, and immediate. QMetry and AWS are making that future real today.

Experience SmartBear AI on AWS

Empower your teams to deliver quality with confidence through the combined strength of SmartBear AI and Amazon Bedrock. Ready to transform your testing workflow? Discover how AI-powered insights can simplify test management, reduce manual effort, and accelerate release cycles while maintaining enterprise grade security and reliability.

Visit AWS Marketplace to explore SmartBear solutions including QMetry, test it out through a free trial, request a demo, and begin your journey toward a smarter, more efficient approach to quality management.

SmartBear – AWS Partner Spotlight

SmartBear is an AWS Advanced Technology Partner with DevOps Competency provides comprehensive quality management and testing solutions for modern software development, including QMetry, which leverages Amazon Bedrock to transform test data into actionable intelligence through AI-powered natural language queries.

Contact SmartBear | Partner Overview | AWS Marketplace

Scaling GenAI Customer Experience on AWS: The Locobuzz Blueprint

Manasvi Jain — Wed, 21 Jan 2026 16:31:44 +0000

By: Nitinn Aggarwal, CTO & Co-Founder – Locobuzz
By Pranav Mishra, Content Marketing Specialist – Locobuzz
By Manasvi Jain, Partner Solutions Architect – AWS
By Umang Arora, Sr. Account Manager, Startups – AWS

Locobuzz

In today’s digital-first world, customer experience (CX) is the ultimate battleground for brands. With every click, comment, and conversation shaping perception, the brands that truly stand out are those that use intelligence to understand customers, powered with AI-driven personalization at scale. As interactions across social media, messaging, reviews, and other channels explode into the billions, enterprises need scalable solutions to deliver real-time, context-aware engagements while maintaining high performance, enhanced security features, and relevance.

Locobuzz, an AWS Advanced Technology Partner, powers 400+ enterprises globally across finance, aviation, government, and consumer sectors. Processing 100 million interactions monthly, Locobuzz applies proprietary GenAI innovations like ResponseGenie, AgentIQ, ContextualPulse (features of the CX suite) and CMOVerse to drive emotionally intelligent, multilingual engagement.

This article explores the Locobuzz blueprint — a cloud-native architecture that integrates AWS services with proprietary GenAI models to orchestrate billions of interactions efficiently, reducing response times and improving customer satisfaction.

The Challenge: Real-Time, Hyper-Personalized CX at Enterprise Scale

Customers expect empathetic, personalized experiences across social media, review systems, and news sites — from Facebook and Instagram to Quora, App Store, and Glassdoor, but traditional CX tools struggle with the volume and variety of unstructured data. This leads to delays, inaccuracies, and lost opportunities. Locobuzz, addresses this with a GenAI-first approach, enabling real-time intent detection, sentiment analysis, and response generation while helping maintain compliance.

Scaling to billions of interactions poses key hurdles like real-time ingestion from diverse sources, low-latency AI processing, elastic scaling for peaks, and cost optimization. Locobuzz’s AWS powered architecture helps address these by providing a resilient, high-performance foundation for enterprise CX.

Solution Overview: Locobuzz’s GenAI-Powered CX Suite on AWS

Locobuzz is architected from the ground up on AWS, enabling seamless processing of 1.2 billion conversations annually with expected 99.99% uptime and data redundancy across multiple Availability Zones. Its modular, microservices-based design leverages AWS for flexibility, allowing rapid deployment of new features and independent scaling of workloads.

Key innovations include:

ResponseGenie uses GenAI to create context-aware, natural customer responses across channels, delivering resolutions with consistent tone and lower agent workload. It taps into a brand’s past interactions in form of a knowledge base, and brand tone.
CMOVerse provides brand intelligence through natural language interfaces and advanced LLMs, revealing why customers behave a certain way and delivering actionable insights for campaigns, product strategy, and brand positioning.
AgentIQ acts as an agent co-pilot, reviewing responses before they’re sent to help facilitate alignment with brand guidelines, protect reputation, prevent negative Customer Satisfaction Score (CSAT) impacts, and boost customer satisfaction.
ContextualPulse uncovers the why behind customer sentiment – decoding tone, intentions, and emotional shifts. It organizes feedback into meaningful themes and benchmarks insights against competitors, enabling brands to spot trends early and make smarter, data-driven decisions.

This architecture combines rule-based precision with LLM depth, powered by AWS for real-time analytics across languages and content types.

Locobuzz leverages AWS services like Amazon Elastic Kubernetes Service (EKS) for orchestration, Amazon Elastic Compute Cloud (EC2) for compute and database hosting, AWS Lambda for serverless processing, Amazon Simple Storage Service (S3) for storage, and Amazon Managed Streaming for Apache Kafka (MSK) for real-time data flows. This facilitates elastic scaling, low-latency inference, and cost efficiency.

Architectural Overview

The Locobuzz architecture on AWS is optimized for high throughput, low latency, and horizontal scalability. Figure 1 provides an overview of the core components and data flows, illustrating how AWS services integrate with Locobuzz’s proprietary GenAI to handle billions of interactions.

Figure 1: Locobuzz Scalable GenAI CX Architecture on AWS

This diagram shows data flowing from edge ingestion via Amazon CloudFront to Amazon EKS-orchestrated microservices. Raw data is streamed through MSK, processed serverlessly with AWS Lambda, and Amazon Bedrock for GenAI tasks. Enriched data is stored in the Amazon OpenSearch Service for search, ClickHouse on Amazon EC2 for analytics, and Amazon EC2-hosted databases for transactions, with Amazon S3 for durable storage. Auto-scaling applies across Amazon EKS, MSK, and Amazon Bedrock.

Amazon Bedrock is the central AI backbone of Locobuzz for unification of data ingestion, signal detection, contextual analysis, and automation into a single scalable core. Meanwhile, Amazon Nova Foundation Models (Amazon Nova) acts as the enrichment layer, delivering these AI outputs through dashboards, agent consoles, workflows, and real-time experiences. This dual architecture makes sure that every Locobuzz capability is both technically robust and operationally intuitive.

Through deep integration with AWS services like Amazon Bedrock & Amazon Nova, the system helps enterprises scale CX innovations more securely and efficiently.

Key Components, Integration, and Optimization

The architecture features an entry layer with enhanced security features and low-latency that ingests customer interactions from multiple channels, orchestrates them via a microservices core, and processes data in real time using a scalable streaming and event-driven framework. Proprietary and cloud-augmented GenAI models enrich the data, enabling sentiment analysis, intent detection, summarization, and response generation, while agent co-pilot and analytics layers facilitate brand-aligned responses and actionable insights. Data is stored across high-performance, scalable databases and search engines, with automated scaling and multi-Region redundancy to optimize cost, reliability, and performance.

Enhanced security features are implemented throughout, with AWS Identity and Access Management (IAM) for authorization and authentication, AWS Key Management Service (AWS KMS) for data protection, and Amazon CloudWatch for monitoring. Locobuzz is ISO 27001:2022 and SOC2 certified, enabling private cloud deployments on AWS with Region-specific Amazon S3 configurations for data residency.

Key Data Flows

Social Media Processing: Brand-related posts and messages are captured via Amazon CloudFront, streamed and cleaned in real time, enriched with sentiment, opinion categorization, and summaries using Amazon Bedrock-powered models and indexed in Amazon OpenSearch Service for instant alerts and visibility.
Live Chat Interaction: Incoming chat messages are routed through Amazon EKS, which retrieves context from Amazon EC2-hosted databases, generates GenAI responses via ResponseGenie, checks them with AgentIQ for brand compliance, and delivers them instantly, with all interactions logged in Amazon S3.

Outcomes and Customer Value

Locobuzz helps enterprises to scale customer engagement without scaling headcount. By unifying support, automating workflows, and facilitating accuracy at every touchpoint, brands can deliver empathetic customer experiences while freeing teams to focus on strategic initiatives.

Our customer demonstrates this transformation in action:

90% fewer active hands-on support while maintaining service quality and responsiveness.
Maintained high levels of accuracy and empathy, making sure every interaction feels human.
100% workflow automation in action, streamlining operations end-to-end.
Dozens of agent hours freed strategic initiatives that drive value.

With Locobuzz, our customer turned operational bottlenecks into business advantages, proving that customer experience at scale can be both efficient and human-first.

Conclusion

Locobuzz’s blueprint on AWS is a proven framework for scaling GenAI CX to billions of interactions, harnessing the power of AWS with innovations for proactive, personalized engagements. This not only helps achieve performance and security, but also helps position brands as CX leaders.

Locobuzz – AWS Partner Spotlight

Locobuzz is an AWS Advanced Technology Partner with over a decade of experience in CXM, serving 400+ enterprises across the globe. The system processes 1.2 billion conversations annually, leveraging proprietary GenAI to deliver real-time insights and engagements for brands worldwide.

AI-Powered Connected Customer Experience using Amazon Connect and MoEngage

Ghandi Nader — Tue, 20 Jan 2026 17:07:25 +0000

By: Nishan Das, Director Tech Partnership – MoEngage
By: Ghandi Nader, Sr. Partner Solutions Architect, Customer Experience – AWS
By: Avinash Venkatagiri, ISV Solutions Architect – AWS

MoEngage

AI-powered connected customer experience platforms like Amazon Connect and MoEngage are transforming how businesses meet rapidly evolving customer expectations. Modern consumers require seamless, personalized interactions across every touchpoint. According to a McKinsey report, 71% of consumers demand personalized interactions, and 76% get frustrated when brands don’t deliver on these expectations.

Customer engagement architectures often operate in silos, with marketing platforms, customer data systems, and contact centers functioning independently. This fragmented approach creates blind spots where valuable customer context is lost during channel transitions, leading to repetitive interactions and missed opportunities. Traditional contact centers, while effective for handling calls, lack the predictive intelligence and omnichannel context needed for proactive engagement.

When the MoEngage customer engagement platform integrates with Amazon Connect, an AI-native contact center from AWS, organizations unlock their customer experience potential. This combination transforms reactive customer service into proactive, personalized experiences that anticipate customer needs across all touchpoints.

This blog explores how the integration of MoEngage and Amazon Connect creates a connected customer experience ecosystem, enabling organizations to deliver context-aware interactions that drive satisfaction and measurable business outcomes.

MoEngage Overview

MoEngage is a comprehensive customer engagement platform that helps businesses analyze user behavior, segment audiences, and orchestrate personalized, omnichannel campaigns. It allows marketers and product owners to engage customers across multiple channels like mobile apps, websites, email, SMS and push notifications. With its AI-powered insights and journey orchestration capabilities, MoEngage helps drive customer retention, increase subscriptions, and fuel growth.

MoEngage is built on AWS and uses key services like Amazon EC2, Amazon MSK, Amazon Athena, Amazon SQS, AWS Lambda and Amazon Personalize to deliver real time customer engagement for our customers. AWS helps MoEngage deliver personalized marketing communications within seconds, while scaling for business of every type – startups with thousands of customers to large unicorns and enterprises with hundreds of millions of customers.

AWS allows MoEngage to deliver on its 99.99% Service Level Agreement (SLA) to its customers in 60+ countries across the globe. MoEngage serves over 1350 customers and profiling over 1.6 billion users each month by leveraging AWS services.

MoEngage also has native connectors built with AWS Communication Developer Services (CDS), which allows customers to send personalized messages to their users in a safe and secure manner, with the required levels of scalability and reach as needed.

Solution Overview

The MoEngage and Amazon Connect integration brings together the strengths of both platforms, creating a comprehensive approach to customer engagement. By combining MoEngage’s data-driven insights and omnichannel capabilities with Amazon Connect’s voice communication expertise, businesses achieve:

Gain a 360-Degree View of the Customer: MoEngage provides a unified customer profile by collecting data from multiple touchpoints. This information is shared with Amazon Connect, giving agents context and insights during customer interactions.

Orchestrate Connected Journeys: Brands trigger Amazon Connect actions based on customer behavior and events tracked in MoEngage. For example, if a customer abandons their cart online, MoEngage initiates a callback through Amazon Connect.

Personalize Voice Interactions: Agents leverage customer insights from MoEngage to personalize their conversations, offering relevant information and recommendations.

Enhance Post-Call Engagement: After a call, Amazon Connect shares engagement outcomes with MoEngage, enabling targeted follow-up campaigns through multiple digital channels.

How does the Integration Work?

The integration involves data sharing and event triggering between the two platforms. The below architecture, in figure 1, highlights the key steps of the joint solution:

Figure 1. Connected Customer Experience Powered by MoEngage and Amazon Connect

MoEngage ingests data through SDK integration, S2S APIs and bulk upload APIs, file imports (CSV, Amazon S3, and SFTP), and partner integrations (CDPs and CRMs) to collect and process user, device, event, and auxiliary data.
MoEngage Segmentation allows users to create subsets of users based on similar user attributes (like location) and user activities (events and event attributes).
MoEngage Campaigns & Channels enables communication touchpoints including mobile push, web push, email, SMS, in-app messages, ad platforms connectors, and web personalization to help marketers and product owners engage their audience across platforms.
MoEngage S3 Export provides periodic exports of all user events and campaign events in JSON format to Amazon S3 buckets with customizable frequencies.
Amazon Connect Customer Profiles ingest customer data exported from MoEngage to Amazon S3 by using Amazon AppFlow. The system creates data flows, configures data mappings to define how source data maps to customer profiles, and sets up integrations that provide periodic updates for building unified customer profiles viewable in the agent workspace.
When agents interact with customers through the agent workspace in Amazon Connect, they access customer profiles to retrieve customer information. Additionally, after each interaction ends, Amazon Connect updates these profiles by adding engagement outcomes and enriching customer data.
AWS Lambda processes the customer records from Amazon Kinesis Data Streams and enriches the profile in MoEngage using API integration.

Use Cases and Key Benefits

Retail
- Abandoned Cart Recovery: If a customer leaves items in their online cart, MoEngage identifies them and triggers a callback via Amazon Connect. Agents then assist the customer in completing the purchase, offering personalized discounts.
- Personalized Upselling/Cross-selling: MoEngage identifies customers based on past purchases and browsing behavior. This information is shared with Amazon Connect, allowing agents to offer targeted upselling and cross-selling opportunities during calls.
- Customer Service for Delivery Issues: If a customer experiences a delay in delivery, MoEngage notifies them and, if needed, triggers a call from an Amazon Connect agent to provide updates and solutions.
- Post-Purchase Feedback and Engagement: After a purchase, MoEngage initiates a survey via email/SMS. If the feedback is negative, an Amazon Connect agent follows up to resolve the issue and ensure customer satisfaction.
Financial Services
- Onboarding Assistance: MoEngage tracks users who have started but not completed the onboarding process. This data is shared with Amazon Connect, enabling agents to call and assist them, reducing onboarding friction.
- Fraud Detection and Verification: If MoEngage detects suspicious activity on a customer’s account, it triggers an immediate callback from an Amazon Connect agent for verification and resolution.
- Premium Customer Support: MoEngage identifies customers and prioritizes their calls in Amazon Connect. Agents are equipped with detailed customer information to provide personalized support.
- Subscription Renewal: For customers with expiring subscriptions, MoEngage sends reminders. If they don’t renew, Amazon Connect agents call to understand the reason and offer renewal incentives.
Media
- Content Recommendations and Engagement: MoEngage analyzes user viewing habits and preferences to recommend content. If a user hasn’t engaged recently, an Amazon Connect agent calls to offer personalized recommendations or exclusive content.
- Breaking News and Alerts: For time-sensitive updates like gate changes for travel/hospitality industries that fall in media segment, MoEngage sends alerts via push/SMS. Amazon Connect initiates a call for urgent information delivery, if the user doesn’t acknowledge.
- Subscription Management and Support: Similar to financial services, media companies use the integration for subscription management. MoEngage identifies users who are likely to churn, and Amazon Connect agents call to offer support or incentives.
- Feedback on new releases: After releasing new content, MoEngage triggers a survey. If the feedback is negative, Amazon Connect agents call for detailed feedback and resolution.

Benefits for Businesses

The MoEngage and Amazon Connect integration provides key benefits for businesses:

Enhanced Customer Experience: By providing a personalized and connected experience across digital and voice channels, businesses improve customer satisfaction and loyalty.
Increased Efficiency: Automation and data sharing streamline processes, reducing manual effort and improving agent productivity.
Improved Retention and Growth: By addressing customer needs and providing personalized offers, businesses reduce churn and drive revenue growth.
Data-Driven Decision Making: Insights from both platforms provide a comprehensive view of customer behavior, allowing businesses to make informed decisions.

Conclusion

In today’s customer-centric marketplace, organizations face a defining moment: continue operating with disconnected touchpoints or embrace integrated AI-powered solutions that deliver connected experiences. Through the MoEngage and Amazon Connect integration, we’ve demonstrated how unified customer intelligence transforms fragmented interactions into personalized journeys.

Organizations implementing this integrated approach have increased retention rates, improved agent efficiency, and driven measurable revenue growth through data-driven engagement strategies. MoEngage’s customer engagement platform complements Amazon Connect’s cloud contact center capabilities, allowing businesses to anticipate customer needs, personalize every interaction, and maintain context across all touchpoints.

Ready to transform your customer experience strategy? Connect with MoEngage and your AWS account team today to schedule a personalized consultation and discover how this integration drives customer satisfaction and business growth for your organization.

MoEngage – AWS Partner Spotlight

MoEngage is an insights-led customer engagement platform built for customer-obsessed marketers and product owners. MoEngage’s powerful analytics, personalization, and AI capabilities give you a 360-degree view of your customers and help you create personalized, impactful journeys across multiple channels like mobile push, email, in-app, web push, on-site messages, and SMS. More than 1,350+ global consumer brands across 60+ countries use MoEngage.

Contact MoEngage | Partner Overview | AWS Marketplace

AWS Specialization Program: 2025 Enhancements and Looking Ahead

Kelly Costlow — Mon, 12 Jan 2026 17:04:43 +0000

By Kelly Costlow Dorantes, Global Specialization Program Benefits – AWS
By Laila Chatelain, Sr. Manager, Specialization Programs – AWS
By Chris Butler, Specializations Program Tech Business Developer – AWS
By Ebrar Erdem, Specializations Program Tech Business Developer – AWS

As cloud technology continues to reshape industries, 2025 marked a pivotal year for AWS Specialization Partners—from implementing agentic AI to enhancing security frameworks, Partners delivered customer innovation at scale.

The impact was recognized at the 2025 AWS Partner Awards, where 82% of award recipients were AWS Specialization Partners—reflecting the exceptional value these Partners consistently deliver to customers through proven expertise and measurable business outcomes.

“New Omdia research shows that AWS Partners can achieve up to a US$7.13 revenue multiplier, and we’re investing in AWS Specialization Partners to accelerate this growth,” said Sachin Vora, Director of AWS Partner Programs. “In 2025, we evolved the program with streamlined validation processes that reduced application time by 70%, enhanced AWS Marketplace visibility through badging and co-sell scoring, and additional marketing funding. These enhancements delivered the benefits our Partners need to scale their practices, and we’re continuing this investment in 2026 to drive even greater business outcomes.”

In this blog, we recap 2025 program enhancements for AWS Specialization Partners and preview what’s coming in 2026 as we continue investing in Partner success.

2025 highlights

Eighty-seven percent of customers highlight AWS Specializations as a top three criteria for Partner selection, with 60% ranking it their primary deciding factor (Canalys). To meet this demand, we made it easier for customers to find the right expertise, and expanded the benefits that help Partners scale their practices:

AWS Specialization badge categories

To help Partners better showcase their validated expertise across specific technologies and use cases, we launched AWS Specialization badge categories. This allows Partners to create Specialization badges at the category level—making it easier for customers to identify and select Partners with the right expertise for their unique business needs.

Figure 1 – AWS Specialization badge categories.

Specialization badging in AWS Marketplace

Building on this momentum, we integrated AWS Specialization badges into AWS Marketplace search results, making Partner validation visible to customers during the buying decision. This allows customers to quickly identify Partners with technical expertise through AWS Marketplace’s AI search.

Customers can use the Buyer’s Guide to learn how to search and engage with an AWS Specialization Partner.

Figure 2 – Product listing with AWS Specialization badge in AWS Marketplace.

Visibility into co-sell opportunities

We launched the AWS co-sell recommendation score, visible to AWS Specialization Partners in AWS Partner Central. The dashboard provides Partners with visibility into co-sell opportunities across industries and regions. With AWS Specializations serving as a key factor in internal AWS Partner recommendation algorithms, this transparency empowers Partners to make strategic decisions about which Specializations to pursue based on their go-to-market priorities.

Additional Marketing Development Funds

We expanded Marketing Development Funds (MDF) to stack with Strategic Collaboration Agreement (SCA) benefits and increased the amount of funding for qualifying Partners, like AI Competency and Managed Services Providers (MSP). Additionally, Partners achieving validation in the SMB Competency or any of the new AI Competency agentic AI categories will receive $25,000 in MDF.

“The MDF support has been instrumental in expanding Rackspace’s market presence through strategic marketing initiatives as an AI Competency Partner,” said Kathleen Schneider, Chief Marketing Officer, Rackspace Technology. “Our focused investments in executive roundtables, customer testimonials, and targeted digital programs have strengthened our enterprise relationships, and we’re excited to build on this success with the additional funding being introduced for Agentic AI categories in 2026.”

AWS PartnerEquip expansion

We scaled AWS PartnerEquip with more ways for Partners to engage with enablement led by AWS experts, with thousands of Partners participating in both live and virtual events globally.

PartnerEquip: Live expanded to four locations—Bengaluru, Seattle, Washington D.C., and London. These in-person sessions delivered advanced technical content through hands-on labs, strategic roadmap insights, and access to AWS experts, with opportunities to participate in executive briefings and roundtables.

We also increased PartnerEquip: Virtual events, which enabled Partners worldwide to receive early access to strategic roadmaps and specialized enablement and technical guidance with multi-language voice over and captioning.

New signature benefits

We introduced 30+ new signature benefits to support Partners’ build, market, sell, and grow strategies through new opportunities, including:

Government Competency Partners gained access to AWS Government Day, a virtual learning event hosted on AWS PartnerEquip: Virtual.
SMB Competency hosted 8 global SMB Summits across NAMER (3), EMEA (3), LATAM (1), and GCR (1).
The AWS MSP Program announced new benefits for Customer Management, Strategic Services and Government Practice.

“The SMB Competency validated our strategy and how we work with small and medium businesses,” said Diego Marques, Head of Professional Services at Darede. “Competencies are important for us because they give us access to resources and programs that help us accelerate our business, sales, and ultimately, help the customers.”

Automated application process

To streamline the application experience for Partners, we launched an automatic validation agent in AWS Partner Central for the AI Competency. This agent-led validation will be expanded to include additional Specializations throughout 2026—automatically processes documentation and provides real-time feedback, reducing application processing time by up to 70%.

New AWS Specialization categories

We launched categories and enhancements across key competencies, including:

AWS AI Competency: Three agentic AI categories to address the growing demand for autonomous systems.
AWS Resilience Competency: Expanded to Software Partners, with three categories to strengthen critical workload protection.
AWS MSSP Competency: Seven security categories to help customers identify Partners with specific security expertise. MSSP Partners also have the option to showcase how they integrate validated ISV solutions from AWS Security Competency Partners into their managed security services.
AWS Government Competency: Three categories aligned with mission-critical government requirements.

Watch: Ruba Borno, VP Global Specialists and Partners announces the categories at re:Invent.

Looking ahead

Building on the momentum of 2025, we’ll continue scaling the AWS Specialization Program to drive innovation and customer success.

Partners can expect enhanced application automation across more Specializations, new MDF categories, and increased AWS Marketplace discoverability features—creating even more opportunities to demonstrate their expertise and drive customer outcomes.

Get started

AWS Specialization Partners combine deep industry expertise with proven technical excellence to deliver real business outcomes across any segment, service, or use case.

Customers: Work with an AWS Specialization Partner

Explore validated solutions and connect with AWS Specialization Partners in AWS Marketplace.

Partners: Become an AWS Specialization Partner

AWS Partners interested in joining the Specialization Program can review the Specialization Programs Guide and access applications in AWS Partner Central (login required).

To learn more about the benefits, review the AWS Specialization Benefits Guide.

Accelerating security response with Tines and AWS Security Hub

Ashok Mahajan — Fri, 09 Jan 2026 16:34:43 +0000

By: Ashok Mahajan, Sr. Partner Solutions Architect, Startups – AWS
By: Charlie Ardagh, Head of Partnerships – Tines

Tines

Modern security and operations teams face the critical challenge of having more security data than ever before, yet they struggle to respond effectively to security issues. This challenge stems from three main factors: alert volume overwhelming the manual process, fragmented visibility across multiple security tools, and the challenge that security risks emerge and evolve faster than teams can manually respond. Without orchestrated data flows and automated correlation, security analysts spend time on manual alert triage and context gathering, leaving little capacity for strategic risk detections or proactive defense, creating potential security gaps that need immediate attention.

According to the IDC 2025 Voice of Security report, nearly 60% of security teams have fewer than 10 members and 55% teams manage between 20–49 different tools. Organizations measure these understaffed teams on incident volume (34.9%), response time (33.2%), and detection speed (31.5%), creating a difficult performance balance in which evolving security risks demand immediate attention, but finite human capacity is constrained by manual processes.

To stay ahead of evolving threats, security and IT leaders must orchestrate and automate response at scale, both within Amazon Web Services (AWS) and across their security tools and services. The question isn’t whether organizations need better security tools, but whether they can afford to keep security professionals focused on repetitive workflows while security risks evolve rapidly.

Turning insights into action with AWS Security Hub and Tines

AWS Security Hub is a unified cloud security solution that prioritizes your critical security issues and helps you respond at scale to protect your environment. It detects critical issues by automatically correlating and enriching security signals from multiple sources, such as posture management (AWS Security Hub CSPM), vulnerability management (Amazon Inspector), sensitive data (Amazon Macie), and threat detection (Amazon GuardDuty).

Tines, an AWS Security Competency Partner, is an intelligent workflow platform trusted by the leading organizations. With Tines, organizations can operationalize AI, integrate their stack, and automate their security response, unlocking productivity and future-proofing how work gets done.

Together, AWS Security Hub and Tines give security operations teams the visibility, context, and speed they need to outpace security risks. Security Hub simplifies and unifies security operations through centralized enablement, transforming security signals into actionable insights through automated correlation and near real-time risk analytics. It provides comprehensive visibility across your AWS environment with crucial context, resource relationships, and potential impact analysis. Tines complements these capabilities with added context and orchestrates flexible, intuitive workflows that cover enrichment, escalation, and remediation. Workflows can be built and adapted by the whole team without heavy engineering effort.

With Tines, teams can:

Enrich alerts with context from AWS and third-party solutions (e.g. CrowdStrike, Wiz, Zscaler, and more).
Consolidate their findings into a single, actionable case view with a full audit trail.
Trigger smarter, full-spectrum workflows that combine automated remediation, rule-based actions, and analyst oversight, all without heavy engineering lift.
Route incidents to the right people in real time, balancing speed with control.

Together, AWS Security Hub and Tines transform security operations from reactive to proactive, helping security leaders prioritize critical issues, respond at scale, and stay ahead of evolving security risks.

Here’s one use case of AWS Security Hub and Tines together addressing an overly permissive Amazon Elastic Compute Cloud (Amazon EC2) security group:

Tines receives Security Hub findings through Amazon EventBridge.
Tines identifies who made the change by correlating AWS CloudTrail with a human resources (HR) database to identify the actor. Tines then updates the Security Hub finding status so analysts know this is already being triaged.
Tines workflow uses an AI agent action to summarize the details of the Security Hub finding and automatically create a Tines case for tracking this investigation.
If the user is known, reach out to them. Otherwise escalate to operations through email, Slack message, or case management assignment within Tines or platforms such as Jira or ServiceNow.
Initiate a chat with the end user who created the vulnerable resource to see if they have an approved security exception to do so.
If a valid exception is confirmed, the finding and case are closed. If not, the workflow can automatically remove the security group ingress rule and notify the appropriate manager. Actions like these are fully configurable based on an organization’s need, automation tolerance, and governance policies.
Tines records the full investigation results and automatically resolves the case when remediation is successful or escalates to human analysts for complex scenarios requiring additional review.

Figure 1: Solution workflow

A real-world example

Jarix started out as a security team within Eron International, a global fintech technology platform supporting payment processors and other regulated financial institutions, where they managed a large AWS environment that receives thousands of alerts every day. They quickly realized that there was an opportunity to expand on this expertise and to deliver cybersecurity as a service to other businesses, and they turned to Tines to help scale. Leveraging AWS Security Hub, Tines, and AI-driven enrichment, Jarix built an automation layer that now serves as the backbone of its managed security services. This framework allowed Jarix to expand from securing Eron International to delivering scalable, AI-enhanced security operations for fintech and payment companies across the region. With reusable workflows and intelligent triage, Jarix helps organizations strengthen their security posture, reduce operational overhead, and gain access to advanced detection and response capabilities – without requiring large, specialized security teams.

According to José Huenumán, Jarix CEO and co-founder, “Before Tines, a single security alert could take 20–30 minutes to investigate and resolve. With Tines, that same process happens automatically in seconds.”

Using multiple orchestrated workflows integrated with AWS Security Hub and other AWS services such as Amazon GuardDuty and Amazon Inspector, Jarix has transformed its entire approach to security operations. One workflow triages security alerts, enriches them with context, and sends summarized results to Slack. Another automatically reviews AWS Identity and Access Management (IAM) policies to detect sensitive permissions, and others manage user access, compliance checks, and device control. The outcome is faster remediation, fewer false positives, and significant time savings that free analysts to focus on strategic work.

“With AWS and Tines,” Huenumán says, “we gave our analysts a way to breathe again—turning hours of manual work into seconds and creating a scalable model we now deliver to our customers.”

Looking ahead, Jarix continues to push the boundaries of what’s possible with AWS and Tines. The team is experimenting with AI-powered validation agents using Model Context Protocol (MCP) to automatically gather additional context from complementary security systems, such as Cloudflare WAF. This approach helps validate findings across multiple layers of their environment and further reduce false positives before they reach analysts—extending the value of AWS Security Hub and Tines orchestration to deliver more accurate, intelligent workflows across their entire security stack.

Delivering stronger outcomes, together

Tines connects AWS Security Hub to your entire security and IT systems to enrich alerts with the data needed to orchestrate and automate resolution. Tines helps customers resolve security risks faster and consistently. Tines has developed pre-built workflows for AWS Security Hub and many other AWS services. These workflows act as a starting point, making it easier for teams to get up and running fast.

Together, AWS Security Hub and Tines deliver:

Faster deployments with ready-to-use workflow templates
Seamless collaboration across AWS and other tools
Flexibility for both engineers and analysts to collaborate with or without code
Scalability to keep pace with evolving security risks and business demands

By combining AWS Security Hub centralized visibility with the orchestration power of Tines, customers can strengthen their security posture without adding headcount or complexity.

The path to proactive security

The threats security teams face aren’t slowing down and neither can the workflows that defend against them. By combining AWS Security Hub unparalleled security visibility with the orchestration power of Tines, organizations can transform how they operate, moving from reacting to alerts to running proactive, intelligent workflows with confidence.

The result is more than merely faster response times. It’s sustainable resilience where teams have the power to outpace security risks and protect their most valuable assets. Tines is a AWS Security Competency and DevOps Competency partner and is available in AWS Marketplace.

Learn more about Tines and AWS Security Hub.

Tines – AWS Partner Spotlight

Tines is an AWS Advanced Technology Partner and AWS Competency Partner that provides intelligent workflow platform to help security and IT teams turn AWS signals into reliable, end-to-end action.

Contact Tines | Partner Overview | AWS Marketplace

Exploration Technology Fusion: Connecting Subsurface Data to HPC Insights

Yuriy Gubanov — Fri, 09 Jan 2026 05:03:06 +0000

By: Andrew Huang, Product Marketing Manager – Seagate
By: Jamie Hisee, Global Partner Manager – Petrosys | Interica
By: Yuriy Gubanov, Worldwide Principal Partner Solutions Architect – AWS
By: Dmitriy Tishechkin, Principal Technical Leader for HPC, AI, and Advanced Compute – AWS

Seagate

The oil and gas (O&G) exploration industry faces an unprecedented data challenge. Modern exploration activities generate massive volumes of complex subsurface data—from high-resolution seismic surveys producing hundreds of terabytes per project to advanced logging tools and monitoring networks contributing additional complex datasets. Many organizations struggle to unlock its full value of this wealth of information because of fragmented workflows, data silos, and the sheer complexity of moving and analyzing petabyte-scale datasets.

In this post, we’re excited to share how Seagate Technology, Petrosys Group, and AWS have collaborated to create an integrated technology fusion that transforms how O&G exploration companies manage, transport, and analyze their most critical data assets. This solution addresses the fundamental challenge of connecting subsurface data to high-performance computing (HPC), dramatically reducing time-to-insight while enabling advanced analytical techniques that were previously impractical.

The challenge: Breaking down data silos in exploration

Traditional exploration workflows suffer from significant discontinuities between data acquisition, management, and analysis phases. Field teams collect vast amounts of subsurface data using sophisticated instruments, but this data often becomes trapped in isolated storage systems. The result is that valuable insights remain locked away, exploration decisions are delayed, and organizations cannot fully benefit from their data investments.
The challenges are multifaceted and interconnected. Modern seismic surveys can generate tens to hundreds of terabytes of data per project, while advanced logging tools, permanent monitoring systems, and real-time acquisition networks add layers of complexity and volume that traditional data management approaches cannot handle efficiently. This volume challenge is compounded by workflow fragmentation, where data typically moves through disconnected systems—from field acquisition devices to on-premises storage, then through various processing and analysis tools. Each transition introduces delays, potential data integrity issues, and operational overhead.

Many organizations possess rich historical archives and current project data but lack the integrated infrastructure needed to apply advanced computational techniques like machine learning (ML), AI-driven interpretation, or large-scale comparative analysis. Additionally, on-premises HPC resources often cannot scale to meet peak computational demands, leading to project delays and suboptimal resource utilization.

The solution: Technology fusion for seamless data-to-insights

Our integrated solution combines three specialized technologies to create a seamless pipeline from data acquisition to actionable insights. At the foundation lies Seagate’s Lyve Mobile storage and data transfer service —high-capacity, ruggedized storage arrays designed specifically for secure physical transport of large datasets. Organizations can lease these devices to efficiently move petabyte-scale data between field locations, data centers, and cloud environments while maintaining data integrity, security, and reducing storage ownership costs with an op-ex model.

Lyve Mobile addresses the last mile problem in data movement, where network bandwidth limitations make cloud transfer impractical for the largest datasets. By providing a reliable physical transport mechanism, organizations can drastically reduce data transfer times and maintain their existing field operations while gaining access to cloud-scale analytics capabilities. The system supports mass import and export services, ensuring that even the most data-intensive exploration projects can benefit from cloud-based analysis without being constrained by network limitations.

The second component is Interica OneView from Petrosys Group, a specialized subsurface data management system that provides intelligent metadata extraction, organization, and workflow management. Interica OneView acts as the orchestration layer, ensuring that data maintains its scientific context and accessibility throughout the entire lifecycle. The platform delivers automated metadata extraction and cataloging capabilities, seamless archiving and recall of exploration data, and workflow management that maintains data lineage and provenance. Integration APIs enable bidirectional data flows with other systems, creating a truly connected data ecosystem. Interica OneView can be deployed both on-premises and in the AWS Cloud, providing flexibility in how organizations structure their data management workflows while maintaining consistent access and control. This hybrid deployment model helps ensure that organizations can optimize their architecture based on specific operational requirements, regulatory constraints, and performance needs.

The third component uses the comprehensive cloud infrastructure of AWS to provide scalable, on-demand compute and storage and make the data ready for AI/ML workloads. Our architecture, shown in Figure 1, integrates two key AWS Solutions: Energy Data Insights (EDI), and Energy HPC Orchestrator (EHO). Energy companies can use EDI, a fully managed AWS offering, to deploy and operate their subsurface data management platform in compliance with OSDU® technical standards, providing the trusted data foundation that complements Interica’s OneView data management and analytics capabilities. After exploration data is stored in the cloud through EDI, it becomes immediately accessible to HPC workflows. EHO then provides an innovative open platform that democratizes access to advanced computing resources, offering a shared marketplace where HPC applications can be created, tested, and deployed without requiring massive capital investment. Meanwhile, companies can use AI/ML services, like Amazon SageMaker and Amazon Bedrock, to apply ML models for automated interpretation and intelligent decision-making directly on their OSDU data. This integrated environment maximizes AWS cloud elasticity by dynamically scaling workloads across CPU, GPU, and Graviton instances while using purpose-built AI/ML infrastructure—helping to ensure that resources are acquired when needed and released when not, driving optimal performance, innovation, and cost-efficiency for energy companies of every size.

By using this cloud-based approach (shown in Figure 1), organizations can scale computational resources dynamically, accessing virtually unlimited processing power when needed while avoiding the capital expenses and maintenance overhead of large on-premises HPC clusters. The elastic nature of cloud resources means that organizations can handle peak computational demands without over-provisioning infrastructure for average workloads.

Figure 1: Conceptual solution architecture for exploration technology fusion

Business impact and results

Organizations implementing this integrated solution have achieved significant improvements across multiple dimensions. The elimination of workflow discontinuities and provision of seamless data access dramatically reduces the time required to move from data acquisition to analytical results. Projects that previously required weeks or months for data preparation and initial analysis can now deliver insights in days, fundamentally changing the pace of exploration decision-making.

Faster access to comprehensive analytical capabilities enables more thorough evaluation of prospects and more informed decision-making. Organizations can apply advanced techniques like ML-driven pattern recognition and large-scale comparative analysis that were previously impractical because of data access limitations or computational constraints. This enhanced analytical capability directly translates to improved exploration success rates and more efficient resource allocation.

The solution also delivers enhanced operational efficiency through automated workflows and integrated data management that reduce manual overhead and eliminate many sources of error and delay. Teams can focus on interpretation and decision-making rather than data wrangling and system integration, leading to better utilization of skilled personnel and faster project completion.

Perhaps most importantly, the solution’s cloud-centered architecture helps ensure that organizations can adopt new analytical techniques and technologies as they emerge. This architectural flexibility provides long-term value and competitive advantage, positioning organizations to benefit from future innovations in geoscience computing and data analysis.

Conclusion

The fusion of Seagate’s data transport capabilities, Petrosys Group’s specialized data management platform Interica OneView, and the AWS cloud HPC infrastructure creates a transformative solution for oil and gas exploration. By breaking down traditional data silos and enabling seamless workflows from field to insight, organizations can unlock the full value of their subsurface data investments while positioning themselves for future innovation and growth. This integrated approach doesn’t just solve today’s data management challenges—it creates a foundation for the future of exploration, where advanced analytics, ML, and AI-driven interpretation become standard tools rather than aspirational goals. As the industry continues to evolve, organizations with integrated, cloud-enabled data-to-insights capabilities will be best positioned to discover new resources efficiently and sustainably. To learn more about implementing this solution for your organization, contact your AWS account team or visit the AWS Partner Network to connect with Seagate Technology and Petrosys Group representatives.

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Seagate – AWS Partner Spotlight

Seagate is an AWS Technology Partner and a global leader offering data storage and management solutions for over 40 years. Lyve Drive Mobile is yet another groundbreaking innovation from Seagate®: a high-capacity edge storage solution that enables businesses to aggregate, store, move and activate their data. Scalable, modular, and vendor agnostic, this integrated solution bundle eliminates network dependencies, so you can transfer mass data sets in a fast, secure and efficient manner.

Contact Seagate | Partner Overview | AWS Marketplace

Say Hello to 247 New AWS Competency, Service Delivery, Service Ready, and MSP Partners Added in December

Nick Paris — Mon, 05 Jan 2026 17:10:21 +0000

By Nick Paris, APN Marketing Manager – AWS Partner Network

The AWS Partner Network (APN) is a global community that leverages Amazon Web Services (AWS) technologies, programs, expertise, and tools to build solutions and provide services for customers.

NEW! AWS Competency Partners

To succeed with cloud adoption in today’s complex IT environment and continue to advance in the future, customers can team up with an AWS Competency Partner.

Team up with our newest AWS Competency Partners:

AWS AI Competency

a2c Cloud | NAMER | Generative AI Consulting Services
Alto Professional Services AB | EMEA | Generative AI Consulting Services
Ancrew Global Services | APAC | Generative AI Consulting Services
Aquarela Cloud Solutions | LATAM | Generative AI Consulting Services
Artisan Studios | NAMER | Generative AI Consulting Services
Aspecta | EMEA | Generative AI Consulting Services
Atomic Computing | EMEA | Generative AI Consulting Services
Atyos | EMEA | Generative AI Consulting Services
BASIS | NAMER | Generative AI Consulting Services
Bespin Global | APAC | Generative AI Consulting Services
Buildstr | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Cloud Bridge | EMEA | Generative AI Consulting Services
Clouxter | LATAM | Generative AI Consulting Services
Coditas Solutions | NAMER | Generative AI Consulting Services
Cronos NV – Scale division | EMEA | Generative AI Consulting Services
DataMax | EMEA | Generative AI Consulting Services
DataMax | EMEA | Agentic AI Consulting Services
Dati | LATAM | Generative AI Consulting Services
Elad Software Systems | EMEA | Generative AI Consulting Services
Elevata | LATAM | Generative AI Consulting Services
Futran Solutions | NAMER | Generative AI Consulting Services
I CLOUD SEVEN | LATAM | Generative AI Consulting Services
Keepler Data Tech | EMEA | Generative AI Consulting Services
MethodData | NAMER | Generative AI Consulting Services
Mphasis | NAMER | Generative AI Consulting Services
NCLOUD THREE INFORMATION TECHNOLOGY | EMEA | Generative AI Consulting Services
NeenOpal Inc. | NAMER | Generative AI Consulting Services
Noventiq | EMEA | Generative AI Consulting Services
NTT DATA JAPAN CORPORATION | Japan | Generative AI Consulting Services
Nub8 | NAMER | Generative AI Consulting Services
Nubity | LATAM | Generative AI Consulting Services
Onepoint | EMEA | Generative AI Consulting Services
Optimum Healthcare IT and eCloud Managed Solutions | NAMER | Generative AI Consulting Services
Persistent Systems | NAMER | Generative AI Consulting Services
Pinnacle Technology Partners (PTP) | NAMER | Generative AI Consulting Services
Rootquotient | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
RSNA Cloud Connect | NAMER | Generative AI Consulting Services
Saltware | APAC | Generative AI Consulting Services
Select Soluções | LATAM | Generative AI Consulting Services
Skillwell Oy | EMEA | Generative AI Consulting Services
SkyGRID Global Technologies | EMEA | Generative AI Consulting Services
Structurit Consulting | EMEA | Generative AI Consulting Services
TRD | LATAM | Generative AI Consulting Services
Uturn Data Solutions | NAMER | Generative AI Consulting Services
VividCloud | NAMER | Generative AI Consulting Services
Zaptoz Technologies | APAC | Generative AI Consulting Services
ZirconTech | NAMER | Agentic AI Consulting Services; Generative AI Consulting Services
Applogika | NAMER | Generative AI Applications
ClickHouse | NAMER | Agentic AI Applications; Agentic AI Tools
Ibexlabs / Labra | NAMER | Generative AI Applications
Mimecast | EMEA | Generative AI Applications
Trend Micro Incorporated | NAMER | Generative AI Applications
Workato | NAMER | Generative AI Applications

AWS Cloud Operations Competency

Cloud TechOn | APAC | Cloud Governance
Futuralis | NAMER | Cloud Financial Management
Logicata | EMEA | Operations Management
LucidPoint | NAMER | Cloud Governance; Operations Management
Cribl | NAMER | Monitoring & Observability

Consumer Goods Competency

Artisan Studios | NAMER | Product Development; Supply Chain; Unified Commerce

AWS Data & Analytics Competency

Dati | LATAM | Consulting Services
Red Oak Strategic | NAMER | Consulting Services
Tameshi | EMEA | Consulting Services

AWS DevOps Competency

apser | EMEA | Consulting Services
Matellio | NAMER | Consulting Services
Docker | NAMER | Continuous Integration & Continuous Delivery

AWS Digital Sovereignty Competency

Fastweb | EMEA | Consulting Services
Noventiq | EMEA | Consulting Services

AWS Digital Workplace Competency

Rocket.Chat | NAMER | Collaboration Platforms
Teramind | NAMER | Collaboration Platforms; Endpoint Management

AWS Education Competency

Adamas Tech Consulting | APAC | Consulting Services
WORKMATES CORE2CLOUD SOLUTION | APAC | Consulting Services
Zero&One | EMEA | Consulting Services
Motorola Solutions | NAMER | Administrative and Operations

AWS Financial Competency

TEKsystems Global Services | NAMER | Capital Markets; General Financial Services
Dynatrace | NAMER | Banking; Insurance

AWS High Performance Computing Competency

HCLTech | APAC | HPC Management

AWS IoT Competency

OMNYS | EMEA | Consulting Services

AWS Mainframe Modernization Competency

Kyndryl | NAMER | Mainframe Workload
PricewaterhouseCoopers | NAMER | Mainframe Workload

AWS Media & Entertainment Competency

Red5 | NAMER | Broadcast; Content Production; Direct to Consumer

AWS Migration and Modernization Competency

Cloudsoft | EMEA | Migration Services
Craftech | LATAM | Migration Services
Kudelski Group | EMEA | Migration Services
TOPPAN Ecquaria | APAC | Migration Services

AWS MSSP Services Competency

Avertech Services | APAC | Workload Security

AWS Networking Competency

SourceFuse | NAMER | Consulting Services

AWS Retail Competency

Bloomreach | NAMER | Advanced Data Insights; Customer Engagement; Digital Commerce

AWS Security Competency

HCLTech | APAC | Identity and Access Management

AWS Small and Medium Business (SMB) Competency

Atomic Computing | EMEA | Small and Medium Business
Cloud TechOn | APAC | Small and Medium Business
Co-Prosperity Information | Japan | Small and Medium Business
Devoteam | EMEA | Small and Medium Business

AWS Supply Chain Competency

HCLTech | APAC | Source

NEW! AWS Managed Service Providers (MSPs)

NEW! AWS Service Ready Products

The AWS Service Ready Program validates software products built by AWS Partners that work with specific AWS services.

Explore our newest AWS Service Ready Products:

Amazon CloudFront Ready Products

杭州分叉智能科技有限公司 | China | Monitoring & Analytics

Amazon EKS Ready Products

RisingWave Labs | NAMER | EKS Anywhere
Zilliz Inc | NAMER | EKS Anywhere

AWS Control Tower Ready Products

Darede | LATAM | Security

NEW! AWS Service Delivery Partners

The AWS Service Delivery Program validates AWS Partners that have deep technical knowledge, experience, and proven success in delivering specific AWS services to customers.

Work with our newest AWS Service Delivery Partners:

Capstone Integrated Solutions | NAMER
Clerion | EMEA
Mindware FZ | EMEA
Necko Technologies | EMEA
Nua Technology | LATAM

Amazon Connect Delivery Partners

axcess.io | APAC
CGI | NAMER
SupportZebra | NAMER
TechX | APAC

Amazon DynamoDB Delivery Partners

Applify | NAMER
eCloudrover Information Technology, ECR(HK) Technology | China
Necko Technologies | EMEA
Wonder Cloudy | LATAM

Amazon ECS Delivery Partners

Aeonx Digital | APAC | Compute; Containers; Serverless Computing
Clerion | EMEA | Compute; Containers; Serverless Computing
Runflow | LATAM | Compute; Containers; Serverless Computing
SupportSages Consultancy Services | APAC | Compute; Containers; Serverless Computing
Switch Software | NAMER | Serverless Computing

Amazon EMR Delivery Partners

Visionet Systems | NAMER

Managed Streaming for Kafka (MSK) Delivery Partners

Egen | NAMER
zeb | NAMER

Amazon QuickSight Delivery Partners

Cloudmates | EMEA
Elastic Move AB / Buzzcloud AB | EMEA

Amazon RDS Delivery Partners

Ampity Infotech | APAC | Amazon Aurora PostgreSQL; Amazon RDS for MySQL
Business Compass | NAMER | Amazon Aurora MySQL; Amazon Aurora PostgreSQL; Amazon RDS for Microsoft SQL Server; Amazon RDS for MySQL; Amazon RDS for Oracle; Amazon RDS for PostgreSQL
Byteridge Software | APAC | Amazon RDS for PostgreSQL
Cloud Driver, SA de CV | LATAM | Amazon RDS for Oracle
CloudJournee | APAC | Amazon Aurora MySQL; Amazon Aurora PostgreSQL
CloudThinker | APAC | Amazon Aurora PostgreSQL
Devopsity | EMEA | Amazon RDS for MariaDB; Amazon RDS for MySQL; Amazon RDS for PostgreSQL
Enreap Systems | EMEA | Amazon RDS for MySQL
Galactic Network | APAC | Amazon Aurora PostgreSQL
IBN Technologies | APAC | Amazon RDS for MySQL
Incedo | NAMER | Amazon Aurora MySQL; Amazon Aurora PostgreSQL; Amazon RDS for MySQL
Minimum Clicks | EMEA | Amazon RDS for PostgreSQL
Nitrowise Labs | EMEA | Amazon Aurora PostgreSQL; Amazon RDS for PostgreSQL
Nusummit Technologies | APAC | Amazon RDS for MySQL; Amazon RDS for Oracle
Phoenix Consulting | EMEA | Amazon Aurora MySQL; Amazon RDS for MariaDB
Pyramid Consulting | LATAM | Amazon Aurora PostgreSQL
Redbee Software | EMEA | Amazon RDS for MariaDB
Solution Design Group | NAMER | Amazon Aurora MySQL; Amazon Aurora PostgreSQL
ZirconTech | NAMER | Amazon RDS for Microsoft SQL Server; Amazon RDS for PostgreSQL

Amazon Redshift Delivery Partners

Applify | NAMER
Genpact | NAMER
Shenzhen Topjoys Technology | China

AWS CloudFormation Delivery Partners

Andersen | EMEA
Applify | NAMER
Novalo Technologies | EMEA
Nusummit Technologies | APAC
Solution Design Group | NAMER
StarOne IT Solutions | APAC
ZiMetrics | APAC

AWS Config Delivery Partners

Nublit by Domus Global | LATAM
Zero&One | EMEA

AWS Control Tower Delivery Partners

Almoayyed Computers Middle East | EMEA | Infrastructure as a Service
Alpha Data PJSC | EMEA | Migration; Networking; Security
CoreScale | LATAM | Security
Frontier Business Systems | APAC | Security
HiveTek Corporation | NAMER | Networking; Security
Mactores Cognition | NAMER | Migration; Security
Nusummit Technologies | APAC | Infrastructure as a Service; Networking; Security
SNS – Saturn Networking Solutions | EMEA | Containers; Infrastructure as a Service; Migration; Networking; Security
Virtuability | EMEA | Security; Serverless

AWS Database Migration Service (DMS) Delivery Partners

Blend | NAMER
ChipReady | NAMER
Devoteam | EMEA
Enuaba Technologies | EMEA
Nublit by Domus Global | LATAM
SourceFuse | NAMER
Sufle | EMEA

AWS Direct Connect (ISV and SI)

CloudThat | APAC

AWS Glue Delivery Partners

Applify | NAMER
CloudJournee | APAC
Darede | LATAM
Five Acts | LATAM
Leega Consultoria e Informatica | LATAM

AWS Graviton Delivery Partners

CloudThat | APAC

AWS IoT Core Delivery Partners

Darede | LATAM

AWS Systems Manager Delivery Partners

AICorePro Technology | NAMER
Cevo Australia | APAC
Grow Cloud | LATAM
Integrationworx | NAMER
Nublit by Domus Global | LATAM

AWS Transfer Family Delivery Partners

Business Compass | NAMER
Encora Holdings | NAMER
HWS Informationssysteme | EMEA

More Value, Greater Profitability for AWS Partners

Learn how AWS is transforming the partner experience >>

How Talkdesk CXA and AWS are Redefining Customer Experience with Agentic AI

Stefan Schneider — Tue, 23 Dec 2025 16:33:13 +0000

By: Suresh Bhandarkar, Chief Technical Architect – Talkdesk
By: Ana Gosseen, Solutions Architect – AWS
By: Pedro Marques, Solutions Architect – AWS
By: Dr. Stefan Schneider, Principal Partner Solution Architect – AWS

Talkdesk

The New Era of Customer Experience

Across industries, customer experience (CX) has become a decisive factor in business performance. Organizations in retail, financial services, healthcare, and the public sector are under pressure to meet rising customer expectations for instant, accurate, and personalized support. Traditional customer experience solutions struggle to deliver this due to siloed systems, disconnected data sources, and involvement of manual processes.

At the same time, advances in Agentic artificial intelligence (AI) and large language models (LLMs) are transforming how organizations engage with customers. Enterprises now have the opportunity to combine automation with human intelligence to enhance accuracy, reduce time to resolution, and solve complex issues.
This is where Talkdesk Customer Experience Automation (CXA) comes in. CXA is an AI-powered orchestration platform that helps organizations deliver smarter, and personalized customer experiences all on the secure and scalable foundation of AWS.

From Conversations to Outcomes

By combining multi-agent orchestration with real-time data access, Talkdesk CXA helps organizations move beyond managing interactions to automating outcomes, turning every customer contact into a moment of value. Talkdesk empowers enterprises to innovate, with low operational costs, and deliver consistent, intelligent, and personalized service at scale. Because CXA is built on AWS, customers benefit from scalability, reliability, and access to a broad suite of services that extend functionality.

Why it matters

Customer experience is no longer a back-office function, it’s a key business driver. Organizations with strong CX outperform competitors in retention, revenue growth, and brand reputation, yet companies struggle to modernize their legacy infrastructure or unify data across multiple touchpoints.

Talkdesk CXA, powered by Agentic AI and built on AWS, helps enterprises close that gap. It transforms customer operations into intelligent, connected ecosystems where automation and human expertise work hand in hand. The result:

Quick resolutions and lower cost-to-serve
Consistent experiences across voice, chat, and digital channels
Actionable insights from every interaction
Higher employee satisfaction and productivity

Built for industries that require seamless customer experiences

Every industry faces unique challenges in delivering seamless customer experiences. Talkdesk has addressed this by creating industry-specific experience clouds, preconfigured solutions optimized for vertical requirements, compliance needs, and customer expectations.

Retail: Farfetch

Global fashion platform Farfetch uses Talkdesk CXA powered by AWS to manage high volumes of customer inquiries during peak shopping periods. With automation and real-time data access, Farfetch reduced handling and resolution times by more than 50%. The result is faster service, happier customers, and stronger loyalty, all without increasing headcount.

Financial Services: WaFd Bank

WaFd Bank modernized its customer experience center using the Talkdesk Financial Services Experience Cloud. By integrating conversational AI and automation into its existing systems, the bank enhanced client engagement while meeting strict regulatory standards. This innovation contributed to Talkdesk achieving the AWS Financial Services ISV Competency, a recognition of the company’s excellence in delivering industry-specific solutions on AWS.

Public Sector and Healthcare

With the recent Federal Risk and Authorization Management Program(FedRAMP) certification, Talkdesk enables government and healthcare organizations to adopt customer experience automation fast and in a secure manner to improve their citizen experience. These sectors require compliant and tightly integrated solutions. Talkdesk’s experience working with AWS backend systems ensures that deployments move from pilot to production quickly while meeting all regulatory requirements.

The Power of Collaboration: Talkdesk and AWS

The collaboration between Talkdesk and AWS goes beyond technology integration; it’s a shared commitment to helping customers innovate as fast as their business grows. As part of the AWS Partner Network, Talkdesk leverages AWS’s global infrastructure and ecosystem to ensure its solutions meet enterprise demands for performance, compliance, and scalability.

Through CXA platform roadmap alignment with AWS AIML services, Talkdesk continuously enhances the intelligence of CXA. This partnership also helps joint customers take advantage of AWS Marketplace for simplified procurement, deployment, and billing.
For example, Talkdesk CXA is available on AWS Marketplace, allowing customers to easily discover, trial, and deploy the solution through their existing AWS accounts.

How Talkdesk CXA works

At its core, CXA uses a containerized multi-agent orchestration framework running on Amazon EKS, designed to streamline every stage of the customer journey. These specialized AI agents, as shown in figure 1, collaborate to handle tasks such as answering questions, retrieving knowledge base entries, updating systems of record, and summarizing interactions.

Figure 1: Talkdesk CXA architecture

Unlike single-bot implementations that operate in isolation, CXA agents share context and coordinate actions. This ensures continuity between channels and touchpoints, so customers never need to repeat information. Human agents remain central to the process, supported by real-time guidance and automation that amplifies their impact.
Because CXA runs on AWS, enterprises take advantage of elastic scalability and continuous innovation. This makes it easier to adapt to changing business needs and customer expectations without rearchitecting core systems.

Customer Impact and Results

Customers deploying Talkdesk CXA on AWS have seen measurable results, including:

On average 50% faster resolution times (Farfetch, Retail)
Improved compliance and engagement (WaFd Bank, Financial Services)
Accelerated deployment timelines in regulated industries through FedRAMP certification

These successes illustrate how Agentic AI, when combined with AWS’s infrastructure, transforms customer service from a reactive function into a proactive, value-generating capability.

Conclusion

Customer experience is evolving rapidly, and businesses need solutions that keep pace. Talkdesk CXA, built on AWS, helps organizations harness Agentic AI to deliver fast, smart, and personalized service across every channel. Through deep integration with AWS services and a shared focus on customer success, Talkdesk is enabling enterprises to move from conversations to outcomes and from customer service to customer excellence.

Call to Action

Organizations ready to transform their customer experience can explore Talkdesk CXA on AWS through the AWS Marketplace or request a pilot directly from Talkdesk. With Agentic AI, AWS, and Talkdesk CXA, every conversation becomes a conversation to deliver for valuable business outcomes.

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Talkdesk – AWS Partner Spotlight

Talkdesk is an AWS Advanced Technology Partner and AWS Competency Partner and has achieved five AWS Competencies in Healthcare, Financial Services, Retail, Small and Medium Business Software and Conversational AI. Talkdesk is a global AI-powered cloud contact center leader for enterprises of all sizes. Talkdesk CX Cloud and Industry Experience Clouds help enterprises deliver modern customer service their way. Their trusted, flexible, and innovative contact center platform leverages AI and automation to drive exceptional outcomes for their customers and improve the bottom line.

Contact Talkdesk | Partner Overview | AWS Marketplace

Revitalizing Indigenous Language with AI: KIWA Digital + Caitlyn on AWS

Kristine Frances Baldas — Mon, 22 Dec 2025 23:20:41 +0000

By: Julie Ryan, CEO – Custom D
By: Tim Ackroyd, Product Manager, Caitlyn – Custom D
By: Kristine Frances Baldas, Partner Solutions Architect – AWS
By: Heena Khan, Partner Solutions Architect – AWS

Custom D

A GenAI Approach to Revitalizing Indigenous Language for the Digital Age

Certain Indigenous languages, like Ngalia, are endangered — spoken today by only a handful of remaining elders. Others, like te reo Māori, still have thousands of speakers but face challenges with intergenerational transfer and digital accessibility. Globally, more than 40% of the world’s 7,000 languages are at risk of disappearing, with one Indigenous language lost every two weeks (UNESCO, United Nations).

Kiwa Digital, a New Zealand–based Indigenous software company, partnered with Custom D, an AWS ISV Partner, to embed Caitlyn, a generative AI platform built on Amazon Bedrock, into its flagship product CultureQ. In this blog, we’ll explore how this modern GenAI solution helps Indigenous communities connect generations through conversation and preserve language with cultural integrity. We’ll also see how Custom D’s Caitlyn platform, powered by AWS, provides a secure and scalable way to support Indigenous knowledge for the digital age.

The Challenge: Preserving Language in the Digital Age

The rapid shift from oral storytelling and written traditions to digital communication has disrupted how knowledge is shared across generations. Younger speakers now engage primarily through smartphones and social platforms, leaving fewer opportunities for immersive, face-to-face learning within their communities. This social transformation has weakened the natural channels through which endangered languages once thrived.

Compounding this challenge is the fact that many Indigenous languages are multimedia-rich, deeply contextual, and reliant on cultural nuance – qualities that don’t translate easily into mainstream digital systems. Imagine asking, “How were bush plants used for traditional medicines?” within the Ngalia people’s body of knowledge. Most search engines or AI chatbots cannot interpret such a question within its proper cultural frame, let alone return a response that is accurate, trustworthy, and culturally appropriate. General-purpose AI models, designed for broad linguistic patterns, often misrepresent or simplify Indigenous knowledge, inadvertently eroding its authenticity.

The dominance of global online platforms and translation tools further marginalizes local languages, prioritizing widely spoken tongues and mainstream narratives. Together, these forces have created deep gaps in intergenerational language transmission. As a result, using digital tools to keep Indigenous languages alive is no longer optional — it is essential for their survival and continued vitality in the digital age.

Solution: GenAI based CultureQ platform

To address the challenge of preserving Indigenous languages and cultural knowledge, Kiwa Digital, in partnership with Custom D, created Culture Q – a conversational platform that enables communities to capture, store and actively engage with their cultural resources.

Unlike static digital archives, CultureQ transforms cultural materials into interactive, multimedia learning experiences. Users can engage through voice or text by asking conversational questions, and even swipe across text to hear the language spoken aloud — turning cultural archives into living, dialogue-based experiences. As shown in Figure 1, users can ask natural-language questions such as “How were bush plants used for traditional medicines?” and receive curated responses that draw from community-approved resources, including recorded stories, language examples, and historical artifacts. This allows people of all generations to explore cultural knowledge in ways that mirror oral storytelling traditions while remaining digitally accessible.

Built on Custom D’s Caitlyn platform, CultureQ combines GenAI with cultural governance to ensure every response is accurate, respectful, and traceable to its original source. The platform has already indexed thousands of artifacts — including over 1,000 digitized audio, text, and visual resources – allowing users to explore them confidently, knowing the authenticity and cultural integrity of each item is maintained.

Figure 1: CultureQ connects generations through digital conversation

How CultureQ powered by Caitlyn Works

The CultureQ solution is built on Custom D’s Caitlyn platform, a generative-AI system that transforms unstructured cultural data into structured, searchable knowledge. Caitlyn orchestrates Amazon Bedrock foundation models, currently using Anthropic’s Claude 4 Sonnet, while retaining the flexibility to integrate newer models as they become available. This design enables continuous improvements in accuracy, performance, security, and cost-efficiency.

Within CultureQ, Caitlyn’s adaptive reasoning layer interprets natural language questions, maps them to approved cultural materials, and generates traceable, context-aware responses. This proprietary framework, developed by Custom D, connects user intent with verified Indigenous knowledge while maintaining cultural integrity.

Intelligent document-processing pipelines, powered by AWS Lambda and AWS Step Functions, manage the ingestion, enrichment, and indexing of text, audio, and visual artefacts. Data is encrypted and stored in Amazon S3, indexed through Amazon OpenSearch Service, and linked via Neo4j to maintain relationships across language, people, and place. Security is reinforced through AWS WAF, Amazon CloudFront, optional AWS Shield, and IAM-based access control with AWS Secrets Manager. System monitoring is achieved through Amazon CloudWatch metrics and alerts, complemented by AWS Security Hub and CloudTrail for compliance and audit visibility.

Architecture Highlights

Deep Cultural Context – CultureQ uses Caitlyn’s agentic context engine, which models connections between ideas, people, places, and language concepts to deliver responses that are culturally aligned and contextually rich. This approach goes beyond conventional retrieval pipelines by interpreting meaning within Indigenous lens.
Metadata & Knowledge Curation – CultureQ leverages Caitlyn’s ability to automatically enrich documents with structured metadata (speaker, language, dialect, geography, cultural context, and rights/consent). This ensures every response is fully traceable to source, preserves historical meaning, and supports long-term language revitalisation.
Data Processing Layer – Amazon S3 provides encrypted, durable storage; Amazon OpenSearch Service enables semantic and keyword search; Neo4j manages knowledge relationships; and Amazon SageMaker supports model evaluation and future training tasks within a machine learning environment.
Enterprise-grade security – A multi-layered security model combines encryption, IAM least-privilege policies, CI/CD vulnerability scanning, and optional dedicated account structures for customers requiring in-region deployments. Authentication uses OIDC-based single sign-on (SSO) with role-based access control (RBAC). Data sovereignty is maintained through regional AWS deployments, ensuring community control of knowledge and access.
Observability Stack – CultureQ tracks system performance through scheduled Amazon CloudWatch metrics allowing developers to analyse trends, refine ingestion quality, and improve model alignment over time.

To date, CultureQ has ingested and indexed more than 1,000 digitized cultural artifacts, maintaining bidirectional traceability between each source material and its derived insights through metadata linking and knowledge base management. By automating context discovery and enrichment, the system reduces manual curation effort and improves search relevance and retrieval speed, helping preserve the cultural relationships and historical meaning embedded within the data.

Figure 2: CultureQ Architecture Diagram

Cultural Integrity as a Design Principle

Because endangered language preservation is not only a technical challenge but also an ethical one, Kiwa Digital and Custom D designed CultureQ with cultural integrity in mind. The AWS Māori Data Lens, developed with Māori community leaders, guided key decisions around data collection, storage, and sharing. Principles such as Kaitiakitanga (guardianship), Manaakitanga (care), and Rangatiratanga (self-determination) shaped how CultureQ manages cultural knowledge — ensuring sovereignty, privacy, and community control.

Caitlyn is positioned as a digital kaitiaki (guardian), surfacing only approved knowledge, with safeguards protecting Indigenous perspectives — all underpinned by AWS Well-Architected best practices.

Caitlyn acts as a digital kaitiaki (guardian), surfacing only approved knowledge while protecting Indigenous perspectives. This is achieved through specific security controls like access management, data encryption, and compliance monitoring — following AWS best practices for secure, reliable, and efficient cloud architecture.

Real-world Impact & Results

In partnership with the Ngalia Aboriginal Corporation, CultureQ now captures stories from the three remaining fluent speakers of the Ngalia language. Learners can interact with these stories in real time through Caitlyn-powered conversation, preserving both language and meaning.

One early use case, the Dreamtime Mamutjitji Story, teaches cultural and scientific insights through a bilingual, interactive experience for children.

“Until now, Indigenous people had limited ways to preserve language and culture, with many unwilling or unaware of how to leverage technology to help. Using generative AI integrated into CultureQ, users can now have conversations and interact with their cultural data in a secure, managed environment, when previously they were only able to access static content.”

— Steven Renata, Managing Board Director, Kiwa Digital

The CultureQ platform helped Kiwa Digital secure the Te Ao Māori & Pasifika AI Award for its innovative approach to supporting Indigenous languages across Aotearoa and Pacific communities.

Measurable Outcomes:

96%+ response accuracy validated through human expert review of generated outputs across text, audio, and visual content
90% less manual work compared to traditional curation methods
Fully cited, verifiable outputs with traceable source attribution
Rapid deployment with secure, serverless orchestration
Active or pilot projects in six Indigenous communities, with expansion planned to 21 by 2026

Conclusion: AI with Integrity

CultureQ shows that generative AI can preserve accuracy, cultural integrity, and privacy – empowering Indigenous communities to protect and share their language and identity on their own terms.

For ISVs and builders, this collaboration offers a practical blueprint for building responsible AI solutions:

Deploy secure, scalable GenAI infrastructure with AWS services like Amazon Bedrock.
Embed AI that adapts to your domain while respecting your data and governance requirements.
Build features that combine code with cultural knowledge, maintaining clear documentation of sources and decisions

This architecture gives organizations a framework that balances scalability, sovereignty, and responsibility, positioning them to deliver AI solutions that earn user trust. AWS Marketplace simplifies deploying this recommended architecture through pre-configured, customizable solutions that accelerate time-to-value while maintaining the flexibility to adapt to your specific requirements. Explore ready-to-deploy solutions on AWS Marketplace, visit Caitlyn.ai to learn more, or contact your AWS account team to discuss implementing culturally aligned AI solutions tailored to your organization.

About Kiwa Digital

Kiwa Digital is a New Zealand-based Indigenous technology company pioneering cultural preservation through innovative digital experiences. Founded in 2003, the company’s expertise evolved from post-production software to developing groundbreaking te reo Māori digital learning applications, establishing new standards in how technology can preserve and promote Indigenous languages and cultures.
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Custom D – AWS Partner Spotlight

Custom D is a New Zealand-based software development company and an AWS ISV Partner. Their flagship product, Caitlyn, is a white-label generative AI platform designed to surface critical insights from complex content in trusted, secure ways—without compromising on accuracy or control. Whether used in cultural preservation, finance, compliance, or agri-tech, Caitlyn helps teams and platforms unlock the full value of their proprietary knowledge that provides an advanced monitoring solution for cloud apps and modern infrastructure that aggregates metrics across distributed services to alert you on service-wide issues and trends in real-time.

Contact Custom D | Partner Overview | AWS Marketplace

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