Business intelligence and analytics

Winner

• Plotly Dash Enterprise, Plotly

Finalists

• FICO Platform, FICO
• Plotly Dash Enterprise, Plotly
• Spotter, ThoughtSpot

From the winner

Dash Enterprise (DE) is an enterprise platform for creating customizable, interactive data applications in Python. Domain experts surface insights and take action through AI-powered development that transforms Python workflows into production apps instantly.

Python-native development builds on existing data science stacks while delivering true customization that creates exactly what stakeholders need, not generic dashboards. Enterprise controls provide built-in security, compliance, and governance, while self-service capabilities eliminate IT bottlenecks. Interactive design enables stakeholders to explore data rather than consume static reports. The transformation shifts teams from analytics support to strategic enablement.

From the judges

“Plotly Dash Enterprise stands out by turning Python workflows into secure, governed, interactive read/write data apps while retaining dev control. The platform is also able to provide contextual intelligence with domain-aware suggestions (across finance, healthcare, and telecom sectors).”

Cloud backup and disaster recovery

Winner

• United Private Cloud, UnitedLayer

Finalists

• Cayosoft Guardian Forest Recovery, Cayosoft
• CloudCasa, CloudCasa by Catalogic Software
• United Private Cloud, UnitedLayer

From the winner

United Private Cloud delivers cloud backup and disaster recovery through a layered, intelligent architecture. It integrates business continuity with high-availability architecture, performance, intelligence, hybrid colocation, and compliance for mission-critical workloads.

Workloads and data are continuously protected via four DR [disaster recovery] strategies. Real-time replication ensures that data is always protected in Tier 3+ data centers across 30+ private cloud regions and 175+ edge sites on five continents. Data is encrypted in transit and at rest.

UnitedLayer’s approach ensures rapid recovery, regulatory adherence, and seamless scaling — backed by repeated industry recognition and trusted by global enterprises for business continuity.

From the judges

“United Private Cloud guarantees zero downtime and minimal data loss through 99.999% high availability and real-time replication. This translates to measurable business value. A proactive, autonomous approach redefines disaster recovery by enabling predictive failure detection and instant restoration at scale.”

Cloud compliance & governance

Winner

Finalists

• Kion Platform, Kion
• Secureframe, Secureframe

From the winner

Secureframe is a comprehensive, AI-powered platform that helps organizations meet security and compliance requirements more efficiently and effectively. With out-of-the-box support for 40+ frameworks, Secureframe helps organizations streamline audits, reduce manual effort, and improve visibility into their security posture.

The Secureframe platform identifies gaps, collects evidence, generates tailored policies, completes risk assessments and access reviews, and monitors compliance progress in real time. Built-in automation handles control testing, validates evidence, recommends remediations, and accelerates questionnaire responses with the help of our proprietary Comply AI engine. Customers report a 26% average reduction in annual compliance costs and complete audits up to 90% faster.

From the judges

“Secureframe applies contextual AI and 300+ integrations to automate evidence validation, remediation, and monitoring across hybrid environments, turning compliance into an intelligent, end-to-end technology solution. Breakthrough capabilities like AI Evidence Validation, Workspaces, and Custom Integrations redefine compliance automation, outpacing competitors with foundational shifts rather than incremental features.”

Cloud cost management

Winner

Finalists

• Stacklet Jun0, Stacklet
• Tangoe One Cloud, Tangoe
• UnityOne AI, UnityOne AI

From the winner

Tangoe One Cloud is an enterprise-grade FinOps [financial operations] platform that unifies expense and asset management across public and private IaaS [infrastructure as a service], SaaS, and UCaaS [unified communications as a service] environments. Designed to align IT, finance, and procurement, Tangoe One Cloud centralizes governance, chargebacks, anomaly detection, and multi-cloud transparency into one scalable platform.

Tangoe’s “Cloud Optimizer” applies machine learning, predictive models, and historical usage data to identify savings opportunities and recommend the most cost-effective cloud infrastructure options. Automated workflows drive cost allocation, charge-backs, and remediation actions, eliminating manual reporting cycles. The platform also delivers real-time anomaly alerts and AI workload governance through a new AI Cost Visibility Dashboard.

From the judges

“This AI-driven, multi-cloud architecture is a highly scalable and technically robust FinOps solution. Tangoe One Cloud’s capabilities set it apart from competitors by enabling real-time governance of AI/ML spending, automated chargebacks, and actionable insights across diverse enterprise infrastructures.”

Cloud security

Winner

• Cortex Cloud, Palo Alto Networks

Finalists

• Aviatrix Cloud Native Security Fabric, Aviatrix
• Cortex Cloud, Palo Alto Networks
• Ivanti Neurons Platform, Ivanti

From the winner

Cortex Cloud rearchitected Palo Alto Networks’ cloud-native application protection platform (CNAPP) on the AI-driven Cortex SecOps platform to deliver a unified user experience with persona-driven dashboards and workflows.

Cortex Cloud identifies and prioritizes issues across the application development pipeline. The platform improves multi-cloud risk management with AI-powered prioritization, guided fixes, and automated remediation. Cortex Cloud natively integrates the unified Cortex XDR agent, enriched with additional cloud data sources, to prevent threats with advanced analytics. Cortex Cloud natively integrates cloud data, context, and workflows within Cortex XSIAM to significantly reduce the mean time to respond (MTTR) to modern threats with a single, unified secops solution.

From the judges

“Cortex Cloud is one of the strongest offerings in cloud security, because it unifies cloud detection and response with CNAPP capabilities on a single AI-driven platform, giving teams real-time, context-rich protection across code, runtime, and cloud environments. By eliminating silos, reducing MTTR, and continuously learning from incidents, Cortex Cloud sets a new standard for enterprise-to-cloud protection.”

Data management: Databases

Winner

Finalists

• Couchbase Capella, Couchbase
• EDB PostgresAI, EnterpriseDB
• Percona Everest, Percona
• Qdrant, Qdrant

From the winner

Qdrant is an open-source, dedicated vector search engine built for an era where over 90% of enterprise data is unstructured. It enables developers to build and support production-grade AI retrieval and vector search across any scale, modality, or deployment. Purpose-built in Rust for unmatched speed, memory safety, and scale, Qdrant delivers up to 40x faster retrieval and >4x higher throughput.

Qdrant is more than vector search: It gives AI agents grounded retrieval “memory” to plan, use tools, and act in real time. With Cloud Inference, hybrid search, and flexible reranking, teams build agentic workflows that stay relevant, responsive, and cost-efficient.

From the judges

“Qdrant is a powerful, technically advanced product that stands out in the highly competitive vector database space. Its deep support for agentic AI, multimodal search, and enterprise-scale deployments make it a foundational tool for the AI era.”

Data management: Governance

Winner

• Actian Data Intelligence Platform, Actian

Finalists

• Actian Data Intelligence Platform, Actian
• Pentaho Platform, Pentaho
• Transcend, Transcend

From the winner

The Actian Data Intelligence Platform centralizes all enterprise meta-data into a single source of truth, enabling effective discovery and utilization of data assets while ensuring regulatory compliance (GDPR, CCPA) and security controls. The cloud-native platform provides a searchable catalog of all data assets with automated metadata collection to ensure proper data documentation and context. Actian’s “governance by design” approach embeds governance into the DNA of every data interaction.

The platform’s innovative knowledge graph delivers deeper, more relevant search results by understanding complex relationships between concepts and entities, enabling faster data discovery, reduced regulatory compliance risk, and confident AI implementation.

From the judges

“The Actian Data Intelligence Platform addresses some of the most critical and persistent challenges in data governance with a highly modern, forward-looking approach. Actian’s platform is well architected and addresses real organizational pain points, including inconsistent data, a lack of trust in analytics, and growing compliance burdens.”

Data management: Integration

Winner

• SnapLogic Agentic Integration Platform, SnapLogic

Finalists

• Rocket DataEdge, Rocket Software
• SnapLogic Agentic Integration Platform, SnapLogic
• Merge, Merge

From the winner

SnapLogic is an all-in-one platform to create, integrate, and orchestrate data products, apps, APIs, and AI agents. Using a low-code, “click-not-code” interface and built-in genAI, SnapLogic empowers both IT and business users to automate workflows, transform data, and create intelligent agents that streamline operations — no matter where the data resides.

With natural-language prompts, anyone across the enterprise — from HR to Finance to Legal — can build IT-approved automations that eliminate repetitive tasks and deliver real business value. For example, legal teams can create IDP [intelligent document processing] agents to redline contracts, while finance teams can automate fraud detection workflows.

From the judges

“By embedding AI-powered agent creation directly into its integration fabric, SnapLogic transforms how enterprises operationalize genAI. With tools like Prompt Composer and Agent Visualizer, it delivers secure, enterprise-grade innovation well beyond traditional data movement platforms.”

Data management: Pipelines

Winner

Finalists

• Airbyte Open Source, Airbyte Cloud, Airbyte Enterprise, Airbyte Enterprise Flex, Airbyte
• DataPelago Accelerator for Spark, DataPelago
• Prophecy, Prophecy

From the winner

Prophecy uses AI agents, a visual canvas, and automatic code generation to help analysts and business experts of all skill levels access the data they need, whenever they need it. An LLM AI assistant for data pipeline creation is powered by a knowledge graph of data sets, schemas, models, and pipelines. The Visual Canvas enables anyone who needs data to build and refine pipelines, using drag-and-drop components for extraction, transformation, enrichment, aggregation, and loading.

Prophecy identifies errors and suggests how to fix them. Visual pipelines are automatically compiled into production-ready code with full Git versioning, documentation, CI/CD support, and lineage tracking.

From the judges

“Prophecy delivers a rare combination of technical depth, user-centric design, and enterprise-grade controls. Its intelligent use of AI assistants, visual editing, and code compilation makes it uniquely powerful, particularly in large organizations seeking secure, self-service analytics at scale. It transforms how data engineering is done.”

Data management: Security

Winner

Finalists

• Bedrock Platform, Bedrock Data
• QuProtect, QuSecure
• Sentra Data Security Platform, Sentra

From the winner

QuSecure’s QuProtect platform is a comprehensive suite of post-quantum cryptographic solutions designed to safeguard data across various platforms and applications. It includes quantum-resistant algorithms, cryptographic agility, key management systems, and secure communication protocols. QuProtect enables visibility into cryptography in use — a new level of insight.

QuProtect provides centralized control of quantum-resilient cryptography, which is ideal for managing and updating cryptographic protocols across the network. Post-quantum algorithms can be implemented and updated centrally, ensuring uniform security policies. QuProtect empowers security leaders with comprehensive visibility, adaptive cryptographic controls, and orchestrated protection to safeguard data against both traditional and emerging threats.

From the judges

“This is one of the most innovative products I have ever seen that offers quantum-resilient protection. It offers security without changing existing systems, which is a great feature to have. I can see this driving a lot of innovation in the cost-effective, post-quantum security of applications for enterprises.”

Data management: Streaming

Winner

Confluent Cloud, Confluent

Finalists

• Confluent Cloud, Confluent
• Hydrolix, Hydrolix
• Lenses, Lenses.io

From the winner

Confluent Cloud offers a fully managed cloud-native data streaming platform, built and operated by the creators of Apache Kafka. This platform empowers teams to easily connect, process, and govern real-time data without the operational burden associated with open source solutions. The result is [shorter] time to insight, lower infrastructure costs, and the ability to build data-driven applications at scale.

On top of trusted features like Confluent Cloud for Apache Flink and Stream Governance, Confluent has introduced capabilities over the past year to unify streaming and batch data processing on a single serverless platform. Confluent Cloud features battle-tested security, compliance, and 99.99% uptime.

From the judges

“Confluent Cloud extends Kafka and Flink into a managed serverless platform that unifies streaming and batch, simplifying real-time data use for AI and analytics. Its innovations — Tableflow, Flink Native Inference, and Flink Search — move streaming closer to enterprise AI workflows, while strong uptime and governance features deliver reliability at scale.”

Devops: Analytics

Winner

• Azul Intelligence Cloud, Azul

Finalist

• Azul Intelligence Cloud, Azul

From the winner

Azul Intelligence Cloud provides actionable intelligence from production Java runtime data that dramatically boosts devops productivity. It supports any OpenJDK-based JVM (Java Virtual Machine) from any vendor or distribution. It consists of three services: Azul Code Inventory, the only solution that precisely catalogs what code runs in production across all Java workloads to accurately identify unused and dead code for removal; Azul JVM Inventory, which uses actionable intelligence available only at run time to continuously catalog running JVMs to help ensure ongoing Oracle license compliance; and Azul Vulnerability Detection, which uses Java class-level production runtime data to detect known security vulnerabilities.

From the judges

“Azul Intelligence Cloud delivers strong impact for Java-heavy organizations. Its class-level runtime approach is more precise than traditional SCA [software composition analysis] tools and yields measurable productivity gains. Overall, a differentiated and timely solution with high relevance for devops productivity.”

Devops: Automation

Winner

Finalists

• Ciroos AI SRE Teammate, Ciroos
• Nutanix Database Service, Nutanix
• Chef, Progress

From the winner

Progress Chef addresses the full spectrum of devops and devsecops, using a single “as code” framework to configure, deploy, and manage virtually any asset on any cloud to any edge, including support for any infrastructure or application, including cloud-native assets like Kubernetes and public cloud services.

Using this framework, the Chef portfolio can help organizations streamline their continuous compliance posture and secure infrastructure support on-premises or in the cloud. Our Policy as Code approach brings configuration management, application delivery, security policy enforcement, and compliance into a single step, eliminating the security silo and moving everyone into a shared pipeline and framework.

From the judges

“Chef has earned its place at the forefront of the devops and devsecops fields. It brings innovative automation, robust security features, and scalability that address the challenges faced by modern organizations, especially in hybrid and multi-cloud environments.”

Devops: CI/CD

Winner

• Buildkite Platform, Buildkite

Finalists

• Buildkite Platform, Buildkite
• CircleCI, CircleCI
• CloudBees Unify, CloudBees
• Harness CI/CD, Harness

From the winner

As the volume, speed, and unpredictability of code creation accelerate with LLM-assisted development, traditional CI/CD pipelines can no longer keep up. Buildkite’s Model Context Protocol (MCP) Server enables real-time, adaptive automation, reducing build times by allowing models to optimize execution order and parallelism while supporting real-time adaptation, failure recovery, dynamic resource allocation, and context-driven decision-making.

Unlike other MCP implementations that wrap APIs or provide prebuild insights, Buildkite’s MCP Server operates inside the pipeline context, turning scripted workflows into dynamic, self-optimizing systems. It acts as a conversational protocol between LLMs and the Buildkite platform, using more than a decade of operational context.

From the judges

“Buildkite brings cutting-edge technology to CI/CD, drives significant business benefits for organizations, and demonstrates true innovation in the space. It’s a powerful tool that not only addresses current challenges but is well positioned to define the next wave of software delivery as AI becomes an increasingly integral part of development workflows.”

Devops: Code quality

Winner

Finalists

• Graphite, Graphite
• Moderne, Moderne
• SmartBear AI, SmartBear
• SonarQube, Sonar

From the winner

SonarQube is an automated code review platform that helps developers deliver high-quality, secure code. It integrates into the CI/CD pipeline on devops platforms for automated, continuous code inspections. Deployable on-prem or in the cloud, it scans repositories for bugs, vulnerabilities, and code quality issues. Seamless integration into IDEs, via SonarQube for IDE, ensures quality and security at the start of development.

SonarQube’s quality gate alerts in real time when there’s something to fix or review in changed or added code, offering real-time guidance. The AI CodeFix feature takes this a step further, leveraging LLMs to automatically generate AI-driven code fixes for discovered issues.

From the judges

“SonarQube’s developer-first approach integrates seamlessly into CI/CD and IDEs, ensuring issues are caught early — before they become costly. The AI CodeFix feature goes beyond detection, offering intelligent one-click fixes. With industry-wide adoption and proven ROI, SonarQube not only supports but elevates modern software development in the age of AI-assisted coding.”

Devops: Observability

Winner

Finalists

• DataBahn, DataBahn
• Grafana Cloud, Grafana Labs
• Honeycomb.io, Honeycomb
• Observo AI, Observo AI

From the winner

Observo AI is an AI-powered data pipeline that transforms how enterprises manage security and observability. It analyzes telemetry data from security and devops tools, using machine learning and agentic AI to flag anomalies; detect privacy risks; perform data transformations; and strip irrelevant data from cloud flow, firewall, operating system, CDN [content delivery network], and application logs.

What sets Observo AI apart is its flexible, non-scripted automation. Instead of asking users to manually write and maintain thousands of inflexible rules, Observo’s ML models automatically detect schemas, summarize normal events, and enrich data with contextual intelligence.

From the judges

“Observo AI addresses one of the hardest problems in security by moving beyond brittle rule-based pipelines to adaptive machine learning and agentic automation. It redefines how telemetry pipelines are built and managed, putting it ahead of competitors that are still bound by static rules.”

Devops: Productivity

Winner

Finalists

• Flow, Appfire
• Cortex Internal Developer Portal, Cortex
• Harness Internal Developer Portal, Harness

From the winner

Appfire Flow is redefining how modern engineering organizations elevate code quality and team performance. As a core component of Appfire’s Software Engineering Intelligence (SEI) platform, Flow helps teams move beyond intuition and manual analysis by delivering real-time insights across the development life cycle. By unifying Git and ticket data from tools like GitHub, GitLab, and Jira, Flow gives teams a clear, objective understanding of where bottlenecks occur, how code review processes are functioning, and how collaboration impacts the quality and veloci- ty of software delivery.

By translating complex engineering data into clear, research-backed insights, Flow makes software delivery understandable and actionable for everyone, from engineers to product leaders to executives.

From the judges

“Flow bridges the gap between engineering work and business outcomes. Its combination of developer empathy, innovation, and actionable insights makes it stand out from legacy tools and rigid DORA [DevOps Research and Assessment]–only dashboards.”

Software development: Platforms

Winner

• Azul Platform Prime, Azul

Finalists

• Azul Platform Prime, Azul
• Uno Platform, Uno Platform
• 1NCE OS, 1NCE

From the winner

Azul Platform Prime is a high-performance Java platform that provides superior speed, startup and warmup, and consistency vs. other OpenJDK distributions to increase responsiveness, reduce cloud compute costs by 20%+, and boost operational efficiency — all without recompiling or changing application code.

With Platform Prime, companies can reduce cloud waste and improve application performance. It works across the most popular Java long-term releases: Java 8, 11, 17, and 21+. It is ideal for demanding, business-critical applications, including those used for massive data sets in distributed data processing.

From the judges

“Runtime-level improvements without application changes are compelling and reduce adoption friction. The cost-savings angle is strong: fewer servers, higher CPU thresholds, and better autoscaling leads directly to opex reduction in cloud spend.”

Software development: Security

Winner

• Chainguard Containers, Chainguard

Finalists

• Chainguard Containers, Chainguard
• SonarQube Advanced Security, Sonar

From the winner

Chainguard Containers is a curated catalog of over 1,500 minimal, vulnerability-free container images that have a reduced attack surface, broad customization capabilities, and improved supply chain integrity for containerized applications. By providing trusted open source software (OSS), built from source and updated continuously, Chainguard helps organizations eliminate threats in their software supply chains.

Chainguard Containers reduces the cost of engineering toil that comes with patching software and strengthens an organization’s security posture by ensuring [that the organization always has] the latest version of the software in production.

From the judges

“Chainguard Containers represents a step-change in how organizations consume open source software by moving from reactive vulnerability scanning to proactively delivering zero-CVE [common vulnerabilities and exposures] images built and maintained continuously. This product is a redefinition of supply chain security, setting a new benchmark for the industry.”

Software development: Testing

Winner

• CloudBees Smart Tests, CloudBees

Finalists

• CloudBees Smart Tests, CloudBees
• Sauce Labs Platform for Test, Sauce Labs
• Harness AI Test Automation, Harness

From the winner

CloudBees Smart Tests is an AI-powered intelligent testing solution built to support enterprise dev-test workloads. It reduces cycle times, improves triage accuracy, and enhances visibility into test behavior across teams.

CloudBees Smart Tests enables faster dev-test iteration with an analytics engine that flags flaky, long-running, and reliable tests, giving both engineers and leaders a shared view of test performance. Predictive Test Selection (PTS) finds failures 40% to 90% faster by running only the most relevant tests. It also offers accelerated test failure resolution through smart classification, pattern detection, and unified session insights, plus automated alerts that keep code owners informed and engaged.

From the judges

“CloudBees Smart Tests reimagines enterprise testing by applying ML to prioritize only the tests that matter, eliminating waste and accelerating feedback loops. Its innovation lies in combining predictive selection with easy integration, turning testing from a bottleneck into a driver of speed, quality, and confidence.”

Winner

Finalists

• Progress Telerik & Progress Kendo UI, Progress Software
• Tabnine, Tabnine
• Warp Agentic Development Environment, Warp

From the winner

Tabnine is the only AI coding assistant purpose-built for enterprise teams with complex codebases, mixed tech stacks, and strict security and compliance requirements. Whether deployed as SaaS, VPC [virtual private cloud], on-prem, or fully air-gapped, Tabnine provides full control over the environment — no outbound connections, no telemetry, no silent updates.

With deep integrations across Git and other SCMs [source code managers], IDEs, and tools like Jira and Confluence, Tabnine understands the entire organizational context, helping teams write, test, document, review, and maintain code faster and more consistently while following teams’ internal standards and compliance rules.

From the judges

“Tabnine is technically strong, enterprise-focused, and full of meaningful differentiators. Its commitment to secure, offline, and customized AI coding assistance gives it an edge in regulated industries. The product clearly aligns with market needs and shows a credible track record of impact.”

About our judges

Ashutosh Datar is a seasoned technology leader specializing in distributed systems, scalable API infrastructure, and intelligent storage. With more than 20 years of experience at companies such as Pure Storage, Hewlett Packard Enterprise, and Nimble Storage, he has led the design of next-generation systems that power large-scale storage and data platforms. At Pure Storage, Datar plays a key role in advancing the Fusion platform — an intelligent, policy-driven infrastructure that unifies storage management across heterogeneous systems.

Anshul Gandhi is an AI and product leader with a track record of building 0→1 systems and scaling 1→N AI platforms across enterprise, consumer, and go-to-market domains. His work bridges cutting-edge research and real-world applications, translating AI innovation into products that deliver exceptional user experiences and measurable business impact. He has led AI strategy and platform initiatives across sectors including healthcare, SaaS, manufacturing, and cybersecurity and holds multiple patents in applied AI.

Sahil Gandhi is a senior data scientist at Amazon and a product-minded AI leader with over a decade of experience in analytics, experimentation, and applied machine learning. He specializes in building and scaling AI-powered data products, including AI agents, RAG-based systems, and enterprise analytics platforms that drive smarter decisions and measurable business growth. He also serves as an Advisory Council Member at Products That Count, contributing to industry leadership and best practices.

Stan Gibson is an award-winning editor, writer, and speaker with 41 years’ experience covering information technology. Formerly executive editor of eWEEK and PC Week and senior editor at Computerworld, he is currently an adjunct analyst at IDC. As principal of Stan Gibson Communications, he writes for many websites, including CIO.com, and is a popular host for online events.

Arun Krishnakumar is a seasoned leader in e-commerce product strategy; digital transformation; and emerging technologies such as AI, machine learning, and blockchain. An author, startup mentor, and master class instructor, Krishnakumar has built and scaled cloud-based web and mobile platforms that serve millions of users. His experience spans customer acquisition and retention, conversion optimization, and sustainable growth, delivering impactful products for multibillion-dollar international brands.

Gaurav Mittal is a software engineer and seasoned IT manager adept at guiding teams in developing and deploying cutting-edge technology solutions. He specializes in implementing innovative automation solutions that unlock substantial cost savings and enhance operational efficiency.

Shipra Mittal is an accomplished IT professional with more than a decade of experience in software engineering, data quality, and analytics. She has advanced from leading client-focused software projects to building and guiding QA and data teams and now focuses on transforming data into actionable insights that enhance business performance. Recognized for her strong technical foundation and commitment to continuous learning, Mittal brings deep expertise in data validation, quality assurance, and analytics innovation to her role as a judge.

Priyank Naik is a principal engineer with more than 20 years of experience in the financial industry, specializing in building complex, real-time distributed, cloud-enabled systems for front-office operations, risk management, and fixed income research. In his current position, he is also involved in integrating genAI for automating financial reporting and forecasting cash flows.

Peter Nichol is a data and analytics leader for North America at Nestlé Health Science. He is a four-time author and an MIT Sloan and Yale School of Management speaker dedicated to helping organizations connect strategy to execution to maximize performance. His career has focused on driving and quantifying business value by championing disruptive technologies such as data analytics, blockchain, data science, and artificial intelligence. He has contributed to CIO.com and has been recognized for digital innovation by CIO 100, MIT Sloan, the BRM Institute, Computerworld, and PMI.

Anton Novikau is a seasoned software development leader with nine years of experience spearheading innovative technology solutions. As head of mobile development at Talaera, an EdTech start-up, Novikau drives the technical vision and execution of transformative learning experiences while pioneering AI integration across the company’s product suite. His expertise spans full-stack development, cloud architecture, and leveraging artificial intelligence to enhance educational outcomes.

Rahul Patil, hailing from the vibrant city of New York, is a seasoned professional in the tech industry with 18 years of extensive experience. Currently working at a hedge fund, he has honed his skills in back-end development with a particular focus on Java. His deep passion for technology drives him to constantly explore and utilize cloud-native services such as AWS and GCP.

Kautilya Prasad is a distinguished expert in software development, specializing in digital experience platforms and artificial intelligence. With more than 18 years of experience driving digital transformation for numerous Fortune 500 clients, Prasad excels at integrating artificial intelligence, digital experience, and data analytics to deliver innovative solutions that elevate customer engagement. He is an active contributor to the technology community, participating in peer reviews and shaping discussions on emerging technologies.

Shafeeq Ur Rahaman is an accomplished leader and researcher in data analytics and digital infrastructure, with over a decade of experience developing transformative, data-driven solutions that drive business performance. As the associate director of analytics and data infrastructure at Monks, he leads global initiatives in data pipeline automation, cloud architecture, and advanced analytics, including the design of mixed media marketing models to optimize campaign effectiveness.

Ramprakash Ramamoorthy leads the AI efforts for Zoho Corporation. He has been instrumental in setting up Zoho’s AI platform from scratch. He comes with a rich 12-plus years of experience in building AI for the enterprises at Zoho. The AI platform currently serves over a billion requests a day and is growing strong. Ramamoorthy is a passionate leader with a level-headed approach to emerging technologies and is a sought-after speaker at tech conferences.

Monika Rathor is a lead application engineer at Level Home, where she is building smart home solutions with smart access, automation, and building intelligence solutions that improve apartment living and management in the most impactful, cost-effective way possible. She is also a performance improvement enthusiast, driven to achieving optimizations like cutting latency from 200ms to just 50ms. Monika also loves mentoring her team, helping them grow and learn.

Isaac Sacolick is a lifelong technologist who has served in CTO and CIO roles and the founder of StarCIO, a digital transformation leadership, learning, and advisory company. He is a writer and keynote speaker and the author of the Amazon bestseller Driving Digital, a playbook for leading digital transformation, and Digital Trailblazer, a career guide for technology and business professionals. Recognized as a top digital influencer, Sacolick is a frequent contributor to InfoWorld and CIO.com.

Scott Schober is the president and CEO of Berkeley Varitronics Systems, a 54-year-old New Jersey–based provider of advanced, world-class wireless test and security solutions. He is the author of three best-selling security books: Hacked Again, Cybersecurity is Everybody’s Business, and Senior Cyber. Schober is a highly sought-after author and expert for live security events, media appearances, and commentary on the topics of ransomware, wireless threats, drone surveillance and hacking, cybersecurity for consumers, and small business.

Kumar Srivastava is a seasoned technology executive and entrepreneur with more than two decades of experience in building and scaling AI-driven platforms across consumer packaged goods (CPG), retail, cybersecurity, supply chain, and digital transformation. Currently serving as chief technology officer at Turing Labs, Inc., he leads the development of the industry’s most advanced AI formulation platform, helping the world’s largest CPG companies accelerate innovation, optimize costs, and bring products to market faster with scientific precision.

DJI, the global leader in civilian drones and creative camera technology, today published a new DJI Enterprise case study demonstrating how the DJI Dock 3 and Matrice 4D drones are protecting communities living in the Peruvian Andes. In cooperation with researchers from Western Norway University of Applied Sciences and Peru’s National Institute of Glacier and Mountain Ecoystem Research (INAIGEM), DJI’s advanced drone technology are transforming geohazard monitoring and warning systems with automated drone flights and cloud-based processing.

Located 4,500 meters above sea level, the Palcacocha Lagoon is surrounded by the Palcaraju and Pucaranra glaciers. Driven by climate change, the lagoon has expanded dramatically due to accelerating melting, increasing the risk of avalanches and flooding for the more than 120,000 people living in the same basin.

Monitoring a High-Risk, Hard-to-Access Environment

Traditional onsite monitoring required physical access to the lagoon and the surrounding glaciers, which was a demanding task. Measurements could only be taken manually and intermittently. This was both challenging and potentially dangerous for field teams due to the steep terrain, limited safety infrastructure, and unstable slopes. With automated drone flights using the Dock 3 and Matrice 4D, observations could be made frequently from a safe distance, allowing researchers to measure glacier velocities, monitor the acceleration of large ice blocks, and detect cracks forming that may signal instability.

Innovation and Data: How automated drones provide valuable

By collecting critical changes early with the Matrice 4D, authorities and local communities could potentially gain valuable time to react, issue warnings, and take preventive measures before a hazardous event occurs. Meanwhile, researchers could use the collected information to advance the understanding of glacier dynamics and glacier–climate interactions in an environment known to be highly sensitive to global warming.

Using optical and thermal sensors, the Matrice 4D Series could capture high-resolution terrain maps, temperature variations, and meltwater flow patterns around the lagoon, glaciers, and surrounding moraine. These datasets are used to generate 3D models of the glaciers and compare against previously-collected data to evaluate progress.

This drone is housed in the DJI Dock 3, DJI’s enterprise “drone-in-a-box” solution that’s purpose-built for remote and automated drone operations. Its weather-resistant design enables reliable performance in extreme conditions, including the Andes Mountains, down to -30°C.

Empowering the rapid data processing is DJI FlightHub 2, which serves as the “brain” of these operations. This cloud-based software allowed researchers to plan, execute, and analyze missions remotely. All flight data is uploaded and processed automatically in the cloud, enabling teams to review results from anywhere in the world. After each mission, the drone returns to the Dock to recharge and prepare for the next scheduled flight, ensuring continuous and consistent monitoring.

Related

---

Support CleanTechnica’s work through a Substack subscription or on Stripe.

---

SM Bicutan, one of the many malls under the ShoeMart chain in the Philippines, is along the flight path as my Korean Air flight lands in Manila. I can see the bright Christmas light array from 200 feet up.

As millions of Filipino families gather beneath towering Christmas trees illuminated by thousands of energy-efficient LED lights at SM Supermalls this holiday season, few realize they’re witnessing renewable energy in action. The spectacular 60-foot Christmas tree at SM City North EDSA, the 50-foot London-themed display at SM Megamall, and the 65-foot pop culture-inspired centerpiece at SM Mall of Asia all share a common power source: the Philippines’ most extensive commercial solar energy infrastructure.

With the 30th Conference of the Parties (COP30) currently convening in Belém, Brazil, to address the planet’s climate crisis, SM Prime Holdings demonstrates that corporate climate action need not sacrifice tradition or spectacle. Instead, the company has engineered a model where sustainability powers celebration—quite literally.

The Fairview holiday magic

In July 2025, SM Supermalls activated what would become the energy backbone for this year’s Christmas festivities: the Philippines’ largest rooftop solar photovoltaic installation at SM City Fairview in Quezon City. The 3.785-megawatt peak (MWp) system spans 4.3 hectares of rooftop space and comprises 6,882 solar panels capable of generating approximately 5,960 megawatt-hours (MWh) annually—enough to power the mall’s operations through the energy-intensive holiday season while offsetting 4,133 tons of carbon dioxide emissions.

The installation, which features energy-efficient LED technology similar to that used in SM’s Christmas displays, supplies 11% of the mall’s total annual energy consumption. This includes the electricity required for the elaborate Christmas decorations, thousands of lights, and the extended operating hours that characterize the Filipino holiday shopping tradition from September through January.

The technical achievement extends beyond capacity. Despite launching during monsoon conditions following two typhoons, the system achieved 85–90% capacity output at activation, ensuring reliable power generation even during the Philippines’ most challenging weather months—precisely when Christmas preparations intensify.

Christmas lights, sustainably sourced

SM Supermalls’ commitment to sustainability extends directly into its Christmas celebrations, with installations like SM Aura’s 33-foot tree adorned with energy-efficient LED lights as part of the mall’s sustainable Christmas showcase. Since 2018, all 85 SM Supermalls have utilized LED lighting systems throughout their facilities—a transition that delivers 50% energy savings compared to traditional lighting while producing the brilliant displays that define Filipino Christmas celebrations.

The mathematics of sustainable celebration are compelling. SM City North EDSA’s Elfdale transformation features a 60-foot Christmas tree surrounded by vibrant digital displays, cascading waterfalls, and elaborate lighting installations. SM Mall of Asia’s Paskong Pinoy celebration showcases grand Filipino-themed installations, including illuminated kalesas, sorbetes carts, and jeepneys flanking a towering Christmas tree. SM Megamall’s 50-foot London Christmas scene features elaborate lighting and tuxedo-donned teddy bears—all powered increasingly by renewable energy.

The visual spectacle extends to SM Seaside City Cebu’s 40-foot Christmas tree and SM Aura’s Sinag at Saya, featuring handcrafted ornaments and parols made from native materials. These installations, launched in mid-October and maintained through early January, represent continuous energy demands that solar infrastructure helps meet.

88 malls, one green grid

SM Prime’s solar commitment encompasses all 88 operational malls nationwide, representing a total rooftop solar capacity of 73 MWp as of July 2025—an increase from 63 MW earlier in the year. The company targets 100 MWp capacity by year-end 2025, positioning SM Prime as holder of the Philippines’ largest commercial solar portfolio precisely as the Christmas season reaches peak energy consumption.

The renewable energy journey began in 2014 when SM City North EDSA became the first commercial property in the Philippines—and reportedly in Southeast Asia—to install rooftop solar PV panels. That pioneering installation now powers part of the same mall where Christmas icon Jose Mari Chan performed during the 2024 tree-lighting ceremony, connecting sustainability infrastructure with cultural tradition.

The progression continued with SM Mall of Asia’s 2.7 MW solar system in 2016, which subsequently received a Sustainable Energy Award from the Department of Energy. The MOA installation now contributes to powering the mall’s elaborate Christmas displays, including its recent All-Star Christmas campaign featuring pop culture-themed installations and performances by Sarah Geronimo.

Most recently, the Fairview installation—at 3.785 MWp—surpassed SM City Santa Rosa’s previous record of 3.088 MWp. Santa Rosa’s system, featuring 5,772 panels across two hectares, generates sufficient energy to power over 2 million mobile phone chargers—a telling metric during the holiday season when families document Christmas memories and share festive greetings.

SM Prime’s renewable energy expansion relies on diversified partnerships with major energy providers, creating a procurement model that extends beyond onsite generation. In 2022, the company secured a long-term power purchase agreement with AboitizPower for clean energy sourced from the Tiwi-MakBan Geothermal Power Plant and PV Sinag’s solar facilities.

In 2023, SM Prime executed an agreement with Citicore Renewable Energy Company to procure 90 megawatts from CREC’s Lumbangan Solar Power Plant in Tuy, Batangas. Most recently, a partnership with TotalEnergies resulted in deployment of a 2 MWp solar PV system at MOA Square, featuring 3,672 panels designed to generate 3,000 MWh annually while reducing carbon emissions by 1,430 tons per year—equivalent to removing hundreds of vehicles from Metro Manila’s congested roads.

These partnerships exemplify the renewable energy procurement strategy that enables SM Prime to source over 50% of its electricity from renewable sources—a milestone achieved in 2022 that directly supports the Department of Energy’s mandate to elevate the Philippines’ renewable energy supply to 35% by 2030 and 50% by 2040.

SM Prime’s renewable energy investments serve its commitment to achieve net zero greenhouse gas emissions by 2040—a full decade ahead of the global 2050 target established by the Paris Agreement. This commitment, developed in partnership with World Wide Fund for Nature Philippines and submitted to the Science-Based Targets Initiative (SBTi), positions SM Prime among the few Philippine corporations adopting this rigorous global standard.

The timing proves significant. As Filipino families begin Christmas celebrations earlier than any other nation—often starting in September—the extended holiday season creates sustained energy demands from elaborate lighting displays, extended mall hours, theatrical performances, and climate control for increased foot traffic. The company’s approach demonstrates that renewable infrastructure can support these traditions while reducing environmental impact.

SM Prime allocates 10% of capital expenditures to sustainability and disaster resilience design, reflecting integration of climate considerations into fundamental business planning. The renewable energy infrastructure serves dual purposes: powering daily operations while enabling the spectacular seasonal displays that have become cultural touchstones.

Tradition meets transition

The Filipino Christmas celebration represents both cultural identity and substantial energy consumption. SM’s 100 Days of Joy campaign, which launched in September 2024, features enchanting light projections across its malls displaying cheerful greetings alongside vibrant decorations. These displays operate continuously for more than three months, creating sustained electricity demands that solar infrastructure helps meet.

The scale of seasonal celebration is substantial. SM City North EDSA’s Elfdale installation transforms an entire atrium into an immersive environment with digital elements, while SM Seaside City Cebu’s Santa’s Cirque World features theatrical performances and attractions including ferris wheels and carousel installations—all requiring reliable power.

The Podium’s Holidays are in Bloom celebration features gold and ivory floral arrangements with elaborate lighting, while SM Aura’s Sinag at Saya showcases handcrafted Filipino ornaments alongside energy-efficient LED systems. These installations balance cultural celebration with technical efficiency, reflecting SM’s articulated commitment to sustainability without sacrifice.

Beyond generation capacity, SM Supermalls participates in the Department of Energy’s Interruptible Load Program (ILP), contributing 110 MW of reserve capacity through 56 malls that utilize backup generator sets during power interruptions rather than drawing from the national grid. This capability proves particularly crucial during the holiday season when grid stress intensifies due to increased commercial and residential consumption.

The dual approach—generating renewable energy while providing demand response capabilities—positions commercial infrastructure as active grid participants. During the Christmas season, when electricity demand peaks across Metro Manila and provincial cities, this infrastructure helps maintain grid stability while supporting the elaborate displays that have become central to Filipino holiday tradition.

While COP30 deliberations in Belém focus on climate finance mechanisms and nationally determined contributions, SM Prime’s infrastructure investments demonstrate real-world implementation of climate commitments. The company’s 73 MWp capacity across 88 properties shows how large commercial real estate portfolios can function as distributed energy infrastructure—a model particularly relevant for rapidly urbanizing nations in Southeast Asia.

The approach addresses what sustainability experts term the “energy trilemma”—the intersection of security, affordability, and sustainability that challenges businesses and policymakers. Through direct procurement partnerships, onsite generation, and grid support mechanisms, SM Prime maintains operational continuity and cost management while reducing fossil fuel reliance. The Christmas season serves as a stress test for this model, demonstrating that renewable infrastructure can support peak consumption periods.

As articulated by Hans T. Sy, Chairman of the Executive Committee of SM Prime, the initiatives reflect scaling of sustainability commitments across the company’s property portfolio. The systematic deployment of solar PV systems, coupled with LED lighting conversion and architectural designs incorporating natural lighting through skylights and windows, represents comprehensive energy management.

Blueprint for corporate climate action

SM Prime’s trajectory from its pioneering 2014 installation to its current 73 MWp capacity illustrates both technical learning and scaling challenges in commercial renewable energy adoption. The company’s target of 100 MWp by end-2025, combined with continued partnership development and new installations, suggests sustained momentum.

For the Philippines—a nation vulnerable to climate impacts including typhoons, sea-level rise, and temperature increases—corporate climate leadership carries particular significance. The country’s unique cultural practice of celebrating Christmas for more than three months creates distinct energy challenges that SM’s renewable infrastructure helps address.

The question now extends beyond whether commercial infrastructure can contribute to renewable energy transitions, but how cultural traditions can be preserved and even enhanced through sustainable infrastructure. As families gather beneath solar-powered Christmas trees this season, they participate unknowingly in a demonstration that celebration and sustainability need not compete.

This holiday season, when Jose Mari Chan’s familiar melodies echo through SM malls and children pose for photos beneath towering Christmas trees, the lights illuminating their memories are increasingly powered by the sun—a quiet revolution in how the Philippines celebrates its most cherished season.

Researchers have developed a nanozyme-integrated hydrogel that directly targets advanced glycation end products (AGEs), a key driver of diabetic osteoarthritis (DOA).

Study: Nanozyme-Integrated Hydrogel Targeting AGEs for Diabetic Osteoarthritis Therapy. Image Credit: Dragana Gordic/Shutterstock.com

The study, published in Advanced Science, demonstrates how a locally delivered, photothermally activated biomaterial can slow joint degeneration, restore cartilage function, and improve mobility in diabetic animal models.

Diabetic osteoarthritis is increasingly recognized as a distinct and more aggressive form of osteoarthritis. Chronic hyperglycemia accelerates the accumulation of AGEs, the damaging compounds formed when sugars bind to proteins, which drive oxidative stress, inflammation, cartilage breakdown, and impaired joint lubrication.

Current clinical treatments, including analgesics and intra-articular steroids, focus primarily on pain relief. They do not address AGE accumulation or the downstream cellular damage that makes DOA difficult to manage, particularly in diabetic patients.

Get all the details: Grab your PDF here!

Building a Targeted Hydrogel Therapy

Looking to address these challenges, the research team engineered a composite hydrogel known as PTC-MP. The material integrates polydopamine-coated tannic acid–cerium nanozymes (PTC) into a sodium alginate-Poloxamer 407 matrix, reinforced with magnesium ions (Mg²⁺).

Each component plays a distinct role. Tannic acid helps suppress AGE formation, cerium nanozymes mimic antioxidant enzymes to neutralize reactive oxygen species, and magnesium ions promote endogenous hyaluronic acid production, improving joint lubrication. Polydopamine enables mild photothermal therapy (mPTT) under near-infrared (NIR) light.

The hydrogel is injectable, mechanically stable, and designed to conform to irregular joint cavities, making it suitable for intra-articular delivery.

Under controlled NIR irradiation, the hydrogel warms to approximately 40-42 °C, a temperature range known to be beneficial for osteoarthritis treatment.

This mild heating enhances cellular uptake of the nanozymes and boosts their catalytic activity without damaging surrounding tissue.

The photothermal effect also induces heat shock protein 70 (HSP70), a protective molecule that reduces inflammation, limits cell death, and helps stabilize mitochondrial function in cartilage cells.

Hydrogel Therapy: Does it Work?

In laboratory experiments, the hydrogel inhibited AGE formation by up to 43.6 % at optimal cerium concentrations.

This effect arises from three complementary mechanisms: scavenging reactive oxygen and nitrogen species, chelating pro-glycation metal ions such as Fe²⁺, and competitively blocking protein glycation sites.

Chondrocytes treated with the hydrogel showed lower oxidative stress, restored mitochondrial activity, and increased expression of key cartilage markers, including collagen II and aggrecan. These findings indicate recovery of cartilage homeostasis rather than temporary symptom relief.

Beyond cartilage protection, the hydrogel addresses lubrication failure, a major contributor to joint wear in DOA.

In fibroblast-like synoviocytes, treatment reduced inflammatory signaling and restored the activity of hyaluronic acid synthase-2 (HAS-2). Magnesium ions further stimulated hyaluronic acid secretion, improving the biochemical environment of the joint.

The therapy was tested in streptozotocin-induced diabetic rats with surgically induced osteoarthritis, a model that reflects severe diabetic joint pathology. Although the authors note this represents Type 1 diabetes rather than the more common Type 2 form, the results were striking.

Micro-CT imaging revealed preserved joint architecture and reduced subchondral bone erosion in treated animals. Histological analysis showed reduced inflammation and maintenance of cartilage matrix.

Functional testing confirmed near-normal gait patterns, with treated rats achieving step lengths similar to healthy controls.

A “3R” Strategy for Diabetic Osteoarthritis

The researchers describe their approach as a “3R” strategy:

• Restrain AGE formation
• Restore cartilage homeostasis
• Reinforce joint repair through improved lubrication

This multi-layered design distinguishes the therapy from previous treatments that have focused primarily on inflammation or pain.

Importantly, the hydrogel demonstrated strong biocompatibility. It remained in the joint cavity for up to 14 days, showed no accumulation in major organs, and caused no detectable tissue damage in safety studies.

The authors note that further work is needed to evaluate long-term biodegradation, ion release, and performance in models of Type 2 diabetes.

However, the study establishes a compelling proof of concept for AGE-targeted, nanozyme-based therapies.

The Study Beyond Osteoarthritis

By directly addressing AGE-driven pathology, this work may open new possibilities for treating other diabetic complications marked by oxidative stress and chronic inflammation.

More broadly, it highlights how innovative biomaterials can combine catalytic activity, mechanical support, and external activation to tackle complicated degenerative diseases.

Journal Reference

Chen, R. et al. (2025, December). Nanozyme-Integrated Hydrogel Targeting AGEs for Diabetic Osteoarthritis Therapy. Advanced Science, e16389. DOI: 10.1002/advs.202516389

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Meta’s social network Threads is expanding the number of topics available through its newer communities feature, the company announced on Monday. This expansion might nudge people to use communities less on Reddit and X.

The platform initially launched over 100 communities in October, including those dedicated to basketball, television, K-pop, books, and more, to better compete with other social networks. With today’s expansion, Threads now has more than 200 communities, with additions that include team-specific spaces like Lakers Threads, Knicks Threads, and Spurs Threads.

The company is testing flairs within the community as well, which is also very common on Reddit. This feature lets you add a customizable label underneath your username. For instance, in the NBA community, you can show off what team you support using flair, or in the books community, you can indicate if you’re an author.

Threads is also testing a “Champion” badge to reward members who are highly engaged in a community. The company said that it is giving badges to only a limited number of people who are highly followed in the community and who converse actively.

The social platform, which competes with Elon Musk’s X and startups like Bluesky, crossed 400 million users in August, two years after its launch. The company also noted this fall that more than 150 million people now visit the site daily.

To date, Threads has been focusing on engaging and retaining users with features like DMs, Group chats, and ephemeral posts, and other additions.

Retrieval-Augmented Generation (RAG) has become the go-to method for building reliable AI applications that depend on external data, since it helps overcome LLM limitations, cuts down hallucinations, and delivers expert-level responses grounded in trusted sources. As interest in RAG rises, so does the need for tools that make it easier to explore, test, and refine different RAG strategies. AutoRAG is one of the newer solutions built for this purpose, automating much of the development workflow so you can experiment with configurations, evaluate pipelines, and pinpoint what works best for your use case.

In this guide we’ll cover how AutoRAG works, and how to create an end-to-end RAG application with this technology.

What is Retrieval-Augmented Generation (RAG)?

The two main pieces of RAG are The Retriever and the Generator. Together these pieces make up the Pipeline of RAG that allows you to get accurate answers to complex queries. The generator will read the context that the retriever found and generates a response based on that. 

RAG is an approach that combines searches for external data with Generative Models to improve accuracy by providing reliable references for model-generated answers. Some applications of RAG include Chatbots, Knowledge Assistants, Analytics Solutions and Enterprise Q&A Systems. 

Key Components of RAG

There are several building blocks that make RAG successful and affect the ability of RAG to provide accurate information. These include The Retriever, The Embedding Model and The Generator.  

• Retriever: The first step in RAG is to index the documents using the retriever, then the retriever will search the index for the relevant chunks of documents.  The method for searching the 
• Embedding Model: Indexes can be based on the similarity of the chunks of the documents through various methods such as Similarity Search, Vector Embedding or Hybrid Retrieval. 
• Generator (LLM): The generator must rely on accurate context in order to produce an accurate response.  This is why it is necessary to utilize the best possible version of the context in order to generate an accurate response. 

What is AutoRAG and When to Use It

AutoRAG is a framework that allows you to create, evaluate, and optimize multiple RAG pipelines quickly. It helps developers quickly test multiple design choices without needing to write custom evaluation scripts. When teams want to compare different retrieval methods, embedding models, chunking strategies, or generations, they use AutoRAG. 

With AutoRAG, developers can quickly iterate through many different RAG configurations because it automatically runs tests and provides an evaluation of retrieval accuracy and best configuration for generating pipelines. When you need systematic evaluation or want to test a large number of pipeline configurations, you should use AutoRAG. 

What Problems AutoRAG Solve?

AutoRAG addresses many of the challenges that make RAG development time-consuming. As RAG projects become increasingly complex, these challenges become even more significant. 

• Pipeline Exploration: It enables experimentation across a variety of similarity metrics, chunking sizes, embedding models, and retrievers without manual code. 
• Evaluation: AutoRAG includes evaluation metrics that include retrieval accuracy, citation correctness, and answer quality, making it easy to assess the effectiveness of RAG pipelines. 
• Optimization: AutoRAG identifies the most effective configurations, which enables teams to make the best decision possible for the configuration of their data and use case.

Preparing to Build: Prerequisites & Setup

Set up a development environment suitable for using AutoRAG to develop an application based on RAG. That means you’ll need to establish Python, dependencies, data, and find out what credentials you need from your LLM or embedding model provider. 

You must have a well-defined Python environment as well as several additional dependencies to be able to successfully run AutoRAG. If you have an unclean or improperly defined environment, you may experience conflicts within your dependencies, and your experiments with RAG will not run smoothly. 

1. Python Setup 

python3 -m venv autorag-venv 

source autorag-venv/bin/activate 

python -m pip install --upgrade pip

• Python 3.10 or higher 
• A virtual environment using venv or conda 
• pip for package installation 

2. Core Dependencies 

Install the following packages as required by AutoRAG: 

pip install AutoRAG pandas langchain sentence-transformers faiss-cpu 

• autorag 
• pandas 
• sentence-transformers 
• faiss-cpu or another vector store backend 
• langchain or langchain-community for LLM integrations 

3. Export LLM / embedding keys 

export OPENAI_API_KEY="sk-..." 

If you are using someother providers, set their env vars similarly.

Building a RAG Application with AutoRAG

To create an application based on RAG using AutoRAG and a knowledge base, proceed through the following main steps. 

• Indexing and Embedding: Each chunk of information will be converted into an embedding and stored in a vector database. 
• Retrieval and Pipeline Experimenting: Create a QA evaluation set if necessary, then run AutoRAG to experiment with different RAG pipelines to determine which produces the best results. 
• Deployment: Use the resulting RAG pipeline to respond to user enquiries. 

Data Ingestion and Preprocessing

Firstly, we’ll load the raw documents:

import json
import os
from pathlib import Path
import PyPDF2

def parse_pdf(pdf_path="data/raw_docs/attention.pdf", out_path="data/parsed.jsonl"):
    os.makedirs("data", exist_ok=True)

    with open(pdf_path, "rb") as f, open(out_path, "w", encoding="utf-8") as fout:
        reader = PyPDF2.PdfReader(f)

        for i, page in enumerate(reader.pages):
            text = page.extract_text() or ""

            fout.write(json.dumps({
                "doc_id": "attention.pdf",
                "page": i + 1,
                "content": text,
                "source": "attention.pdf",
                "title": "Attention Is All You Need"
            }) + "\n")

    print("Parsed PDF → data/parsed.jsonl")


parse_pdf()

The following method would babe used for splitting and chunking documents:

import json

def chunk_text(text, chunk_size=600, overlap=60):
    words = text.split()
    i = 0
    chunks = []

    while i < len(words):
        chunk = words[i:i + chunk_size]
        chunks.append(" ".join(chunk))

        if i + chunk_size >= len(words):
            break

        i += chunk_size - overlap

    return chunks


def create_corpus(parsed="data/parsed.jsonl", out="data/corpus.jsonl"):
    with open(parsed, "r") as fin, open(out, "w") as fout:
        for line in fin:
            row = json.loads(line)
            chs = chunk_text(row["content"])

            for idx, c in enumerate(chs):
                fout.write(json.dumps({
                    "id": f"{row['doc_id']}_p{row['page']}_c{idx}",
                    "doc_id": row["doc_id"],
                    "page": row["page"],
                    "chunk_id": idx,
                    "content": c,
                    "source": row["source"],
                    "title": row["title"]
                }) + "\n")

    print("Created corpus → data/corpus.jsonl")


create_corpus()

The following code normalises metadata:

import json

def normalize_metadata(path="data/corpus.jsonl"):
    rows = []

    with open(path) as fin:
        for line in fin:
            obj = json.loads(line)

            obj.setdefault("source", "attention.pdf")
            obj.setdefault("title", "Attention Is All You Need")

            rows.append(obj)

    with open(path, "w") as fout:
        for r in rows:
            fout.write(json.dumps(r) + "\n")

    print("Metadata normalized.")

normalize_metadata()

Indexing / Embedding + Storage Setup

Now, we’d be using the embedding model:

import json
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
import os

def build_faiss_index(corpus="data/corpus.jsonl", index_dir="data/faiss_index"):
    os.makedirs(index_dir, exist_ok=True)

    texts, ids = [], []

    with open(corpus) as f:
        for line in f:
            row = json.loads(line)
            texts.append(row["content"])
            ids.append(row["id"])

    model = SentenceTransformer("all-MiniLM-L6-v2")
    embeddings = model.encode(texts, convert_to_numpy=True)

    faiss.normalize_L2(embeddings)

    index = faiss.IndexFlatIP(embeddings.shape[1])
    index.add(embeddings)

    faiss.write_index(index, f"{index_dir}/index.faiss")
    json.dump(ids, open(f"{index_dir}/ids.json", "w"))

    print("Stored FAISS index at", index_dir)


build_faiss_index()

Vector store or database setup: 

vectordb: faiss_local
index_dir: data/faiss_index
embedding_module: sentence_transformers/all-MiniLM-L6-v2

Retrieval and RAG Pipeline Experimentation with AutoRAG

Firstly, we’ll be creating an QA Evaluation Dataset:

import pandas as pd
from autorag.data.qa.schema import Raw, Corpus
from autorag.data.qa.sample import random_single_hop
from autorag.data.qa.query.llama_gen_query import factoid_query_gen
from autorag.data.qa.generation_gt.llama_index_gen_gt import make_basic_gen_gt
from llama_index.llms.openai import OpenAI

def create_qa():
    raw_df = pd.read_parquet("data/parsed.parquet")
    corpus_df = pd.read_parquet("data/corpus.parquet")

    raw_inst = Raw(raw_df)
    corpus_inst = Corpus(corpus_df, raw_inst)

    llm = OpenAI()

    sampled = corpus_inst.sample(random_single_hop, n=140)
    sampled = sampled.make_retrieval_gt_contents()
    sampled = sampled.batch_apply(factoid_query_gen, llm=llm)
    sampled = sampled.batch_apply(make_basic_gen_gt, llm=llm)

    sampled.to_parquet("data/qa.parquet", index=False)

    print("Created data/qa.parquet")

create_qa()

Running AutoRAG evaluation:

configs/default_rag_config.yaml

node_lines: 

- node_line_name: retrieve_node_line 

nodes: 

- node_type: semantic_retrieval 

top_k: 3 

modules: 

- module_type: vectordb 

vectordb: faiss_local 

index_dir: data/faiss_index 

embedding_module: sentence_transformers/all-MiniLM-L6-v2 

- node_line_name: post_retrieve_node_line 

nodes: 

- node_type: prompt_maker 

modules: 

- module_type: fstring 

prompt: | 

Use the passages to answer the question. 

Question: {query} 

Passages: {retrieved_contents} 

Answer: 

- node_type: generator 

modules: 

- module_type: openai_llm 

llm: gpt-4o-mini 

batch: 8

Use the following code for running the evaluator:

from autorag.evaluator import Evaluator 

evaluator = Evaluator( 

qa_data_path="data/qa.parquet", 

corpus_data_path="data/corpus.parquet" 

) 

evaluator.start_trial("configs/default_rag_config.yaml")

Running the RAG: 

User Query: “What is multi-head attention in the Transformer model?”

Best Practices & Recommendations

Building a robust RAG application involves careful planning. Here are some best practices: 

• Keep original text with embeddings: You should always keep the inode content of any chunk or reference to the inode content along with the embeddings for that content chunk – do not depend solely upon the embeddings. 
• Chunk sensibly: Chunking is done in way that preserves meaning at the boundaries of chunks when being processed; therefore, you should use the same tokenization and the same size for all chunks of content (e.g., 300 to 512 tokens) and maintain overlap of chunks (e.g., 50 overlapping tokens). 
• Cache embeddings: Cache identical embeddings for all content when sending a repeated query or when processing large amounts of content. 
• Monitor quality: Monitor retrieval and generation performance using standard metrics; for example: AutoRAG provides standard metrics for assessing retrieval F1, recall, and correct answering; you can use these metrics to identify any problems with the performance of your RAG system. 
• Secure your keys: Protect your API keys or model tokens; do not hard-code your keys into your application; instead, make use of environment variables or secure vaults. 

By following the above guidelines, development teams will be able to build robust and reliable RAG applications that take advantage of AutoRAG’s automation and yield high-quality results. 

Conclusion

The design of a RAG application consists of multiple components between data processing and retrieval techniques. AutoRAG enables developers to automate the experimental phase and evaluation processes to make the creation of RAG applications simpler. With AutoRAG, developers can quickly experiment with various pipeline designs and launch a superior RAG application based on conclusive data. 

By following the instructions provided within this document, users will be able to produce a dependable, accurate, and ready-to-use application with the implementation of optimal methods. Utilizing AutoRAG’s optimization capabilities and incorporating established best practices, teams have a greater opportunity to create the most beneficial AI experience while reducing the need for time-consuming manual configuration. 

Frequently Asked Questions

 

We’re proud to share that Mumbai has been selected as the newest site — and our first international location — in Cisco’s 40 Communities initiative. This announcement builds on more than three decades of partnership and purpose-driven innovation in India, marking an exciting new chapter in our shared journey to Power an Inclusive Future for All.

For 30 years, India has been central to Cisco’s story. Our commitment to building critical infrastructure, advancing security and AI capabilities, and accelerating digital skills has grown alongside the country’s remarkable economic transformation in support of the Government of India’s growth and development priorities. Since 2015, we’ve collaborated with our partners across government, academia, and nonprofits to positively impact more than 50 million lives in India — a tremendous milestone we reached earlier this year.

Now, we’re building on that foundation in a fundamentally new way. As India’s financial capital, Mumbai is the economic engine of Maharashtra, India’s second-most populous state. Mumbai’s economic significance and diverse talent pool coupled with Cisco’s longstanding partnerships made it the right choice as the second community — and first international market — in our 40 Communities initiative.

 

An integrated approach to transformation: What 40 Communities means for Mumbai

40 Communities represents our next big ambition: a 10-year commitment to engage, support, and invest in 40 communities around the world, bringing together Cisco’s technology, global reach, and trusted partnerships to build connected, resilient communities where everyone can thrive.

What sets this ambition apart is our commitment to a proximate, community-centric approach. We start by listening to understand key needs, opportunities, and existing successful solutions. Working shoulder to shoulder with local leaders and partner organizations, we will partner with the community to co-create a set of integrated solutions that reflect what communities tell us they need the most. By weaving together our expertise across workforce development, connectivity, and security, we help drive coordinated action that delivers exponential impact and lasting change.

For Mumbai, this integrated approach aims to address the most pressing needs and unlock its greatest opportunities. Alongside government leaders, industry partners, academic institutions, and community organizations, we’re initially focused on three areas where Cisco’s capabilities can drive the greatest impact: expanding access to skills in cybersecurity, AI, and networking; supporting startups and small businesses; and building cyber resilience.

Skills training at scale

In partnership with the Confederation of Indian Industry (CII), we’re launching the Cisco Centre of Excellence for AI, Networking, and Entrepreneurship. Located at CII’s Multi-Skill Training Institute and Model Career Centre in Mumbai, the Centre aims to train 100,000 students over three years through in-person and hybrid programs. The physical space includes hybrid classrooms, innovation zones, and a fully equipped makerspace — a collaborative workshop where students can prototype, experiment, and build with emerging technologies — focused on developing the AI and networking skills employers are seeking.

We’re also expanding Cisco Networking Academy across degree colleges, technical institutes, and polytechnics throughout Maharashtra, providing access to courses in cybersecurity, AI, networking, and data analytics.

Through our longstanding collaboration with NIIT Foundation, we’re scaling AI and cybersecurity training, aiming to reach 2.7 million people across India by 2028, including 100,000 in Maharashtra. This builds on our Cyber Suraksha program, which has already helped more than one million people learn cybersecurity skills to navigate an increasingly digital world.

Supporting startups and small businesses

In partnership with Dr. D. Y. Patil College of Engineering and Innovation in Pune—Maharashtra’s second-largest city, about 150 kilometers southeast of Mumbai—we’re establishing an EDGE Center, a hub designed to expand access to digital training, hands-on technology, and entrepreneurial support. The center will equip learners, startups, and small businesses across the state with Cisco’s technologies, training, and mentorship to strengthen local innovation and economic opportunity.

We’re also launching an urban tech accelerator with Villgro Innovation Foundation focused on tech-enabled solutions for water and waste management, one of Mumbai’s most pressing challenges. Participating startups will work directly with Cisco technical experts for support in security, system integration, and cloud optimization.

What connects all of this work is people. Across all three focus areas, we’re creating meaningful opportunities for Cisco employees to engage directly with these initiatives. Our team members will provide mentorship to students at the CII Centre, offer technical guidance to startups in the accelerator programs, and support government officials in building cyber response capabilities. These connections create pathways not just for skills transfer, but for the kind of learning and relationship-building that strengthens both our business and the communities we serve.

 

Shared vision, lasting impact: Co-creating solutions for a brighter tomorrow

Thirty years of partnership in India and 50 million lives positively impacted over the last decade alone have reinforced a simple truth: when we listen to communities, work alongside them, and co-create solutions, technology becomes a catalyst for opportunity, innovation, and resilience.

Mumbai, as the second location under our 40 Communities initiative, represents the next chapter in this story, offering a blueprint for how integrated, tech-enabled solutions can address systemic challenges at scale. We’ll be working closely with our partners to refine and launch these initiatives, measure impact, and share what we learn.

As we usher in this new era of social impact and bring our Purpose of Powering an Inclusive Future for All to life in Mumbai and beyond, we’re not just investing in technology. We’re investing in people, in partnerships, and in the belief that when business and communities thrive, we all move forward together.

Communications service providers are preparing to discuss the key business issues in Denmark. TM Forum, the global alliance of telco and tech companies, will hold its annual DTW – Ignite Global Summit from 17-19 June, 2025, at the Bella Centre in Copenhagen.

TM Forum says it is the only industry body to count the world’s top ten CSPs, and all the key hyperscalers as active, strategic members. With over 800 members, it says it is “on a mission to reinvent the telco industry” as a “vibrant part of the digital landscape”.

DTW (Digital Transformation World) Ignite offers networking opportunities for over 5,000 professionals in the technology, telecoms and AI industries. It promises a “dynamic hub” where industry leaders, visionaries and experts come together to exchange ideas and insights that “will shape the AI-enabled future”.

The event features an agenda including panel discussions, breakout sessions, and interactive workshops, and attendees are being promised numerous opportunities to network and collaborate. Beyond formal sessions, DTW Ignite provides informal spaces, such as lounges and meet ups, to foster “meaningful conversations in a relaxed atmosphere”.

Who’s coming

Companies attending include the key organisations shaping the future of the wider communications world. They include: DOCOMO Euro-labs, Entel Chile, Ericsson, Ernst & Young, F-Secure, FASTWEB, Fierce Network, Gamma Telecom, GCI, Globe Telecom, Google Cloud, GSMA, Hong Kong Telecommunications, Huawei, IBM, IFS, Indosat Ooredoo Hutchison, Infosys, Inspur, Iridium Satellite, Iron Mountain, Jazz, Jio Platforms, KPN, Liberty Global Services, Lumen, MATRIXX Software, Mavenir Systems, Microsoft, MTN, Nokia, Nvidia, Ooredoo Group, Oracle, Orange, PCCW Global, Pegasystems, Proximus, PwC, Rakuten Group, Red Hat, Safaricom, Salesforce, ServiceNow, Singapore Telecommunications, SK Telecom, Swisscom, T-Mobile, Tata Communications, Tech Mahindra, Telecom Italia, Telefónica, Telenor,Telia, Teradata, Turk Telekom, TURKCELL, Verizon, Virgin Media O2, Vodafone Group, Volt Active Data, Whale Cloud Technology, ZTE and many others.

Last year

Last year, TM Forum’s Executive network, TEN100, convened CEOs, CTOs, CIOs and CFOs to “take collective leadership” for three new strategic “missions”, developed by TM Forum members to underpin growth:

Composable IT and Ecosystems – delivering a plug and play, “AI-fluent ecosystem”

Autonomous Network Operations – enabling seamless, intelligent automation at scale

Data and AI Innovation – unleashing new revenue streams

“From the main stage to private roundtables, the telecoms industry stepped up to the business challenge. We saw a crisper focus, more courage and optimism, ready to reimagine our value proposition in the emerging AI era, to create new value by opening our capabilities,” says Nik Willetts, the chief executive of TM Forum. “It’s clear that DTW – Ignite is the place where the industry convenes to co-create answers. The time for talk is over – we need action.”

Excellence awards

Entries for the TM Forum’s Excellence Awards are now closed, and winners will be announced on Tuesday 17 June, 2025, during DTW Ignite. Since their inception, the Excellence Awards have recognised and celebrated innovative achievements spanning the TM Forum’s Open Digital Architecture (ODA) implementation, network monetisation and partnering, customer experience, autonomous networks, AI innovation, and benefits to people and the planet.

“As a neutral, non-profit organisation, our awards ensure a prestigious and impartial view, celebrating the success of key industry players across six categories,” say the organisers.

Growth at the Forum

The TM Forum is a growing organisation, demonstrating its relevance to the evolving communications industry. Orchest, a connectivity and automation specialist, is one of the latest organisations to join, as a service provider member.

As a member, Orchest is integrating TM Forum’s OpenAPIs into its automated platform and implementing the zero-wait concepts from TM Forum’s Autonomous Operations Framework, contributing to the digital ecosystem. This will enable companies to achieve “complete automation” using the Orchest Automation platform, while providing functionalities through standardised APIs from the Metro Ethernet Forum (MEF) and TM Forum.

Orchest Automation offers solutions including a network quoting tool, an installation tracker, service inventory, business intelligence, tech support management, e-commerce, network geo-visualisation, API enablement and management, ERP and CRM deployment, and billing and invoicing control.

At the end of 2024, Orchest says it successfully automated network infrastructure in Indonesia for last-mile quoting, achieving its “first success story” outside of Latin America, by leveraging its “robust” address validation algorithm.

“Joining TM Forum is an important milestone for Orchest as we continue our automation journey, where standardisation is crucial,” says Jeremy Villalobos, the chief executive of Orchest. “The TM Forum ecosystem complements our vision and objectives, and we look forward to contributing and supporting the ecosystem to help more companies achieve exceptional levels of efficiency.”

George Glass, the chief technology officer of TM Forum, adds: “We welcome Orchest at a pivotal moment for the telecom industry. As automation becomes a cornerstone of transformation, Orchest’s expertise aligns with our mission to drive open, interoperable, and AI-powered networks. Its commitment to integrating open APIs and advancing autonomous operations will help accelerate industry-wide efficiency and innovation, supporting our collective goal of a more agile, customer-centric digital ecosystem, that unlocks growth in both value and purpose.”

Billing innovation

Another advocate of TM Forum standards and a prominent attendee at DTW Ignite is MATRIXX Software. A provider of billing and monetisation solutions for CSPs, it recently launched its MATRIXX Dynamic Billing platform.

With the offering, MATRIXX is expanding its technology to incorporate high-performance billing and invoicing on the same platform, so that service providers can “effortlessly” generate precise customer bills. As a result, says the vendor, service providers are able to “alleviate the burden” of costly legacy systems, deliver “financial precision” to the business, reduce the complexity of billing audit and reconciliation, and “enhance customer experience” with a complete, accurate view of spending and billing information at all times.

MATRIXX is able to do this by extending its architecture to unify all charging, rating and billing processes, by combining its patented real-time transactional platform with mass data processing capabilities, eliminating complexities, issues and costs that stem from typical siloed environments.

The new platform is able to manage pricing, charging and revenue data across every stage of the billing journey, from quote and order to consumption, charging, invoicing and payments, delivering accurate billing and balance visibility to a service provider’s customers, finance teams and channel partners alike. MATRIXX Dynamic Billing can process all usage and non-usage charges for retail, enterprise and wholesale lines of business, and for any combination of payment methods, including prepaid, postpaid and pay now.

MATRIXX remains committed to cloud native principles and its TM Forum Ready for ODA (RfODA) status. With MATRIXX Dynamic Billing, new capabilities will be architected to be aligned with the ODA component definitions for Bill Calculation, Bill Generation and Billing Account Management. This ensures that service providers can integrate and scale one or all of the MATRIXX billing microservices cost-effectively, eliminating the need for expensive customisations that result in vendor lock-in.

By delivering all pricing, charging and billing microservices on a unified platform, MATRIXX Dynamic Billing can eliminate common data discrepancies and points of failure in the revenue management domain, that slow revenue recognition and increase the burden in audit, reconciliation and fraud management processes.

“It’s a win-win for the communications service provider and vendor communities alike when operational software is aligned with the Open Digital Architecture principles,” said TM Forum’s Glass. “The whole industry benefits through increased business agility when software companies like MATRIXX commit to supporting the ODA standards.”

Comment on this article via X: @IoTNow_ and visit our homepage IoT Now

The study, published in Biochar X, reports that the catalyst reaches a low overpotential of 250 mV at 10 mA cm² and remains highly stable for more than 50 hours when operating at elevated current density. These performance levels point to a viable, low cost alternative to the precious metal catalysts typically used in large-scale water splitting.

“Oxygen evolution is one of the biggest barriers to efficient hydrogen production,” said corresponding author Yanlin Qin of the Guangdong University of Technology. “Our work shows that a catalyst made from lignin, a low-value byproduct of the paper and biorefinery industries, can deliver high activity and exceptional durability. This provides a greener and more economical route to large-scale hydrogen generation.”

Transforming Lignin Into a Functional Carbon Framework

Lignin is one of the most abundant natural polymers, yet it is often burned for minimal energy return. In this work, the team converted lignin into carbon fibers using electrospinning and thermal treatment. These fibers serve as a conductive and supportive framework for the metal oxide particles. The resulting catalyst, known as NiO/Fe3O4@LCFs, contains nitrogen-doped carbon fibers that offer fast charge transport, high surface area, and strong structural stability.

Microscopy revealed that the nickel and iron oxides form a nanoscale heterojunction within the carbon fiber structure. This interface plays a central role in the oxygen evolution reaction by helping intermediate molecules bind and detach at optimal rates. Pairing these metal oxides with a conductive carbon network improves electron movement and prevents the particles from clumping together, which is a frequent issue in conventional base metal catalysts.

Verified Activity Through Advanced Testing

Electrochemical measurements showed that the material performs better than catalysts containing only one metal, especially under the high current conditions needed for real world electrolysis systems. The catalyst also exhibits a Tafel slope of 138 mV per decade, indicating more rapid reaction kinetics. Additional evidence from in situ Raman spectroscopy and density functional theory calculations supports the proposed mechanism, confirming that the engineered interface efficiently drives oxygen evolution.

Scalable Design Using Widely Available Biomass

“Our goal was to develop a catalyst that not only performs well but is scalable and rooted in sustainable materials,” said co-corresponding author Xueqing Qiu. “Because lignin is produced in huge quantities worldwide, the approach offers a realistic path toward greener industrial hydrogen production technologies.”

The findings underscore the increasing value of biomass-derived materials in energy conversion applications. Combining renewable carbon supports with carefully designed metal oxide interfaces aligns with global efforts to create low cost and environmentally friendly clean energy technologies.

The researchers note that this method could be adapted to different metal combinations and catalytic reactions, opening new opportunities for designing next generation electrocatalysts based on abundant natural resources.