Startups operate in an environment defined by compressed timelines, limited resources, and high expectations.

Small engineering teams take on work that once required full organizations. They are responsible for building embedded software, electronics, cloud systems, and AI systems, while ensuring safety, reliability, and scalability, all while moving at startup speed.

The challenge grows in industries like electric mobility, robotics, aerospace, medical devices, industrial automation, and energy systems. These products are no longer simple mechanical machines or standalone applications. They are intelligent cyber-physical systems that combine software, electronics, physics, connectivity, and increasingly, AI.

As complexity increases, the traditional “build first, debug later” approach begins to break down. Model-Based Design (MBD) and Model-Based Systems Engineering (MBSE) help teams manage this shift and maintain momentum.

The Startup Reality

Startups encounter the same set of engineering challenges. They operate under aggressive timelines with small teams, while managing constant changes in requirements. They often have limited validation infrastructure, yet still face pressure from customers and investors. As complexity increases, hardware and software integration becomes harder, iteration cycles speed up, and processes become difficult to scale.

In the early phase, teams move quickly, but their workflows are often fragmented. Software development progresses separately from hardware, controls validation happens late, and full system behavior only becomes clear after physical integration.

As the product evolves, however, interactions between components begin to drive performance. For example, battery behavior impacts charging performance, sensor latency affects autonomy, and embedded software timing influences safety. Mechanical constraints also begin to influence software behavior.

The system no longer behaves as a collection of independent parts. It behaves as a tightly coupled whole. At this stage, teams either adapt their engineering approach or face increasing integration challenges, rework, and delays.

What is Model-Based Design?

Model-Based Design is an engineering approach where executable models become the core of development instead of disconnected documents and handwritten code. With MATLAB and Simulink teams can model system dynamics, develop control algorithms, and simulate real-world operating conditions early in the process. They can validate functionality before hardware is available, generate production code, and continuously test software as development progresses.

This approach changes when and where teams find problems. Instead of waiting for physical testing, issues surface during simulation, when they are faster and easier to fix. A bug identified at the model stage may take minutes to resolve. The same issue, if discovered after hardware integration, can take weeks.

For teams working under tight timelines, that shift fundamentally changes how efficiently they can build and deliver products. For startups, the difference can be survival-level important.

Model-Based Design is the systematic use of models throughout the development process that improves how engineers deliver complex systems.

Why Startups See Immediate Impact

Large organizations usually have dedicated validation teams, integration labs, testing infrastructure, and enough process maturity to absorb engineering inefficiencies. Startups most often do not. Each prototype requires time and budget, delays directly affect runway, and engineering missteps limit the time available for innovation. Model-Based Design helps startups reduce these risks by enabling virtual development before expensive physical prototyping.

Ather Energy, one of India’s leading EV startups, illustrates this approach in practice. Shivaram N.V., Senior Systems Engineer at Ather Energy, explained “We had lots of promising ideas, but as a small startup, we did not have the time, money, or people to build prototypes to test each one.” Ather Energy engineers use MATLAB and Simulink to model electric scooters, charging systems, embedded software, and system behavior. Instead of building prototypes for every concept, they relied on simulation to guide development decisions. “With Model-Based Design, we identified and validated the best ideas through simulation,” finished Shivaram N.V.

Expanding the View with Model-Based Systems Engineering

Model-Based Design addresses simulation, controls, and embedded development. As products grow more complex, teams also need a structured way to make system-level decisions. This starts with systems thinking—looking at the product as an interconnected whole rather than as a collection of individual components. Systems thinking helps engineers understand dependencies, trade-offs, and the ripple effects that design decisions can have across the entire product. This leads to better decision-making, reduced integration issues, and more robust, reliable designs.

Model-Based Systems Engineering (MBSE) brings this systems perspective into an engineering framework. MBSE helps teams define requirements, structure system architecture, manage interfaces, and understand how decisions affect the overall product by answering key questions about system behavior and interactions.

As startups grow, this structured approach becomes increasingly important. Without it, critical system knowledge often remains with a few experienced engineers. That may work in the early stages, but it becomes difficult to scale as products and teams grow. MBSE helps organizations formalize system knowledge early, improving collaboration, simplifying onboarding, strengthening traceability from requirements to implementation, and enabling teams to manage increasing product complexity with greater confidence.

Engineers use model-based systems engineering (MBSE) to manage system complexity, improve communication, and produce optimized systems.

Connecting MBD and MBSE

The real transformation happens when Model-Based Systems Engineering and Model-Based Design are connected into one engineering workflow. MBSE defines the system at a high level. It captures requirements, architecture, and interactions between components. Model-Based Design brings those definitions to life through simulation, algorithm development, and validation.

Together, they create a connected digital engineering workflow from concept to deployment. This connection allows teams to simulate systems before building them, validate requirements throughout development, identify integration issues early, and reduce reliance on physical prototypes. It also improves collaboration across teams and accelerates embedded software development. Most importantly, it helps teams make better engineering decisions faster.

Faster Validation Changes Everything

One of the biggest advantages of Model-Based workflows is the ability to support continuous validation. In traditional approaches, teams often delay validation until hardware becomes available, creating late-stage surprises.

Model-Based workflows shift validation earlier and make it an ongoing activity rather than a final step. Teams can evaluate system behavior at multiple stages using Model-in-Loop, Software-in-Loop, Processor-in-Loop, and Hardware-in-Loop testing, while also incorporating continuous integration, automated regression testing, and virtual commissioning as development progresses. This allows teams to test functionality continuously instead of waiting for final system integration.

As a result, teams reduce rework and improve engineering confidence. Exponent Energy, an EV energy startup focused on rapid charging systems, provides an example of this approach. The team uses Model-Based Design workflows to develop charging technology capable of delivering ~15-minute EV charging.

Achieving this level of performance requires extensive system simulation, controls validation, and coordination across multiple engineering domains. In this context, relying solely on physical testing would significantly slow development and limit the team’s ability to iterate efficiently.

AI, Simulation, and the Future of Startup Engineering 

The next generation of successful startups will rely on more than coding speed. They will depend on stronger engineering intelligence. This shift brings together AI, simulation, system architecture, digital engineering practices, automation, virtual validation, and model-based workflows into a more integrated development approach.

Teams are adopting these methods because they improve execution, not simply because they are new. Execution determines whether a concept becomes a production product. Teams that establish system-level engineering discipline early, while still maintaining the speed and flexibility needed in early stages, are better positioned for success.

That balance is not easy to achieve, but Model-Based Design and Model-Based Systems Engineering provide a practical way to support it.

Final Thoughts

Innovation depends on more than generating ideas. Innovation is about converting ideas into reliable products faster than competitors. That requires faster iteration, clearer collaboration, early validation, and a strong understanding of system behavior.

Model-Based Design and Model-Based Systems Engineering support these goals. For modern startups building intelligent products, simulation-driven engineering is no longer optional. It is rapidly becoming the engineering foundation for scalable innovation.

Indian Institute of Technology (IIT) and Indian Institute of Science (IISc) startup ecosystems are increasingly focused on frontier engineering domains—electric mobility, autonomous systems, AI‑driven engineering, semiconductors, space‑tech, and climate technologies. What sets these ecosystems apart is not just sector choice, but the depth of innovation: sustained IP creation, systems‑level engineering, and simulation‑led development. 

Accelerators and incubators within these institutions are evolving beyond traditional startup support. They now act as bridges between research laboratories and industrial‑scale deployment, enabling startups to tackle complex, high‑impact problems across mobility, energy, healthcare, aerospace, and national infrastructure. 

Through our work with deep‑tech startups across India, particularly those emerging from institute‑backed accelerators, we observe consistent patterns in what helps teams translate research excellence into deployable, scalable systems. 

The Problem: From Research Breakthroughs to Deployable Systems 

Deep‑tech startups face a fundamentally different challenge from software‑only ventures. Success depends not only on novelty, but on whether complex engineering systems perform reliably in real‑world conditions. 

Common early‑stage challenges include: 

• Solutions optimized for laboratory conditions rather than field environments 

• Late discovery of system‑level constraints such as compute, power, cost, safety, or reliability 

• Difficulty moving from experimental prototypes to repeatable, testable, and certifiable designs 

These challenges are especially acute for founders transitioning directly from academic research into startup mode—a path that is increasingly common within IIT and IISc ecosystems. 

Why This Matters Now 

The stakes for getting this transition right are higher than ever. System complexity is rising, customers expect earlier validation, capital efficiency matters more, and global competition sets the benchmark for quality and safety. 

As a result, early engineering decisions—architecture choices, validation strategies, and iteration cadence—have a disproportionate impact on long‑term outcomes. These decisions tend to compound over time, either accelerating progress or creating costly rework. 

What IIT and IISc Founders Consistently Get Right 

1. Problem‑First Innovation Rooted in Rigorous Science
   Many IIT and IISc founders begin with unsolved scientific or engineering problems—improving autonomy safety, extending battery life, reducing energy losses, or increasing the reliability of embedded intelligence. Their differentiation lies in strong grounding in first‑principles modeling and early use of simulation → validation → optimization cycles. Rather than iterating only in the market, these teams iterate in the lab, the model, and the system, reducing downstream risk when solutions encounter real‑world complexity. 

2. Tight Coupling of Research, Prototyping, and Productization
   IIT and IISc ecosystems support strong lab‑to‑market pipelines, often enabled by access to faculty IP, advanced testbeds, and deep domain expertise. The strongest teams excel at translating PhD‑grade research into applied engineering and moving efficiently from early lab validation to field‑ready prototypes. These startups act as translators of research into deployable systems, not merely product builders. 

3. Systems Thinking Over Point Solutions
   Many IIT and IISc startups operate in domains such as EV platforms, ADAS stacks, aerospace systems, semiconductors, and robotics—where isolated features provide limited value. Founders consistently demonstrate multi‑domain thinking across controls, AI, hardware, embedded software, and cloud systems, along with the ability to design end‑to‑end architectures. Their competitive advantage often lies in system‑level performance and robustness rather than any single component. 

4. Simulation‑Led Innovation as a Core Enabler
   A recurring pattern across successful deep‑tech startups is the early and sustained use of model‑based design and simulation‑driven development. This enables teams to iterate faster, reduce reliance on expensive physical prototyping, and validate behavior more safely—particularly in mobility, aerospace, and healthcare. Many IIT‑ and IISc‑born startups are adopting simulation‑first engineering approaches. Teams in electric mobility reduce physical testing cycles through model‑based workflows, while startups in advanced air‑mobility rely on simulation to explore complex flight‑dynamics behavior before committing to hardware. Across these teams, founders consistently highlight MATLAB and Simulink as critical to accelerating experimentation, improving product quality, and reducing engineering waste—capabilities especially valuable in deep‑tech domains with long gestation cycles. 

   Example from the Field: When Research Meets Real‑World Constraints

   One deep‑tech startup we worked with showed strong early results from an algorithm validated under controlled conditions. As the team prepared for pilots, real‑world variability exposed sensitivity issues, system constraints became clearer, and integration revealed unexpected bottlenecks. Instead of applying incremental fixes, the team introduced system‑level modeling to explore trade‑offs between performance, robustness, and computational cost. Simulating a wider range of operating scenarios early helped them simplify the architecture, improve stability, and align milestones more closely with customer expectations—turning a research‑driven prototype into a more deployable system without compromising the core innovation. 

5. Resilience for Long Gestation Cycles
   Deep‑tech startups from IIT and IISc ecosystems are generally comfortable with longer commercialization timelines. They prioritize IP creation and technical defensibility over rapid but shallow scaling. Accelerator support often reflects this through patient capital, grant‑plus‑incubation models, and strong industry and government linkages. In these contexts, success is driven by depth and defensibility, not speed alone. 

Where MathWorks Fits in This Ecosystem 

Across IIT‑ and IISc‑born deep‑tech startups, engineering tools play a strategic role—not as productivity add‑ons, but as foundational enablers of disciplined, system‑level engineering. 

Many teams adopt model‑based design to manage complexity early and reduce downstream risk. Using MATLAB and Simulink, startups move from theoretical models to deployable implementations while maintaining traceability across modeling, simulation, testing, and validation. This is especially important as AI becomes an integral part of engineered systems rather than a standalone software component. 

Increasingly, startups are also leveraging AI and generative AI workflows within MATLAB to accelerate engineering tasks—such as algorithm exploration, model refinement, data analysis, and design iteration—while keeping engineers in the loop. When AI components are developed in the same environment as control logic, signal processing, and physical models, teams can more easily design, integrate, and validate AI behavior within end‑to‑end systems. 

This unified approach helps teams: 

• Explore design trade‑offs—including AI‑driven behaviors—before committing to hardware 

• Validate system performance and robustness under diverse operating scenarios 

• Shorten iteration cycles while improving reliability, safety, and quality 

For small teams operating in multi‑domain environments—mobility, aerospace, energy, and healthcare—this continuity from classroom to lab to startup to production enables AI‑enabled innovation without sacrificing engineering rigor. 

Closing Takeaway 

The success of IIT and IISc founders in deep‑tech entrepreneurship is not accidental. It stems from the combination of deep science, systems thinking, and simulation‑led validation, supported by accelerators that function as engineering ecosystems rather than funding hubs. 

As India enters its next phase of deep‑tech growth, progress will depend on how effectively we bridge academic depth with industrial scale—and how quickly we translate rigorous ideas into reliable systems that perform in the real world. 

Leela Pawashe has spent her career working in that gap. Across academia, startups, and large medical device companies, she has focused on measuring complex physical systems and helping teams understand what their data actually means.

Today, through her startup Bay Area Sonic Solutions, she is bringing that experience to early-stage teams, starting with ultrasonic devices, but grounded in principles that apply far beyond a single technology.

A Broader View of Ultrasound

When Pawashe talks about ultrasound, she is not just talking about the imaging system you might see in a doctor’s office. She is referring to a broad and rapidly evolving technology landscape spanning medical imaging, therapeutic ultrasound, and industrial inspection.

Ultrasound is sound at a frequency humans cannot hear, typically above 20 kilohertz. That range supports applications such as breaking up calcified lesions in blood vessels or enabling new approaches to neuromodulation and drug delivery. Despite the diversity of use cases, many of these technologies share common measurement challenges.

“One of the things I love about measurements is that no matter what type of device you are developing, there are similar principles you can apply,” Pawashe explains. “It’s about understanding what parameters actually matter for your application.”

From Standards Committees to Startup Founder

Before starting her company, Pawashe built a career at the intersection of ultrasound physics, measurement, and standards development. Her work spans ultrasound imaging, elastography, and cavitation physics, as well as the head of acoustics at a pioneering intravascular lithotripsy company that later grew rapidly and was acquired.

Alongside her industry work, she serves on the IEC TC 87 Ultrasonics Committee, which develops international ultrasonic measurement standards, and she is the international project leader for the IEC 63612 standard focused on characterizing ultrasonic short-pressure pulse therapy. “That combination of industry experience and standards work really shaped how I think about measurements,” Pawashe says. “You want measurements that are rooted in standards, but you also need flexibility, especially when you are working on something new.”

Leela Pawashe sets up her testing rig for ultrasound measurement. (Image courtesy of Bay Area Sonic Solutions)

Why Early Measurement Matters

Across her work with startups and multinational companies, Pawashe has seen a consistent pattern. Teams often delay ultrasound testing until late in development, either because early testing feels premature or because validated studies are expensive and slow. “What I kept seeing was that there are very few options for early-stage exploratory measurements,” Pawashe describes. “Those services can be expensive, they can have long lead times, and they are often designed for finalized systems, not early prototypes.”

Waiting too long increases risk. Without early characterization, teams may discover late in the process that a device exceeds key thresholds, sometimes just before a regulatory submission or a clinical study. Early exploratory measurements give engineers a way to answer those questions sooner, when design changes are still manageable.

“These early measurements help de-risk projects,” she says. “They help engineers find out earlier if they are above certain thresholds instead of discovering issues right at the end.”

From Experience to a New Kind of Service

That gap between early development and late-stage testing is what led Pawashe to found Bay Area Sonic Solutions. “I spent most of my career measuring and characterizing devices that other people were building,” explains Pawashe. “Over time, it became really clear that there was a need to modernize ultrasound measurement techniques and to make them more accessible, especially for early-stage technologies.”

Bay Area Sonic Solutions provides exploratory ultrasound measurement services for teams with functional prototypes who need fast, meaningful data to guide early decisions. The approach is flexible, but it remains rooted in international standards, so results can support longer-term goals as devices mature.

Even for emerging applications where formal standards do not yet exist, Pawashe focuses on repeatable, principled measurements that help teams understand what their devices are doing and what to expect as development progresses.

Let Engineers Focus on Building

For many teams, building the measurement infrastructure can be as challenging as building the device itself. Pawashe sees her role as removing that friction. “My customers would rather be developing the iPhone than measuring the iPhone,” she says.

By outsourcing early measurements, engineers can focus on iteration and design while still gaining insight into device behavior. That clarity helps teams make decisions earlier and move forward with confidence.

MATLAB at the Center of the Workflow

MATLAB has been a constant throughout Pawashe’s career, from early research experiences to academia and industry, and to her work today as a founder. It remains central to how she processes and interprets ultrasonic measurement data.

MATLAB is used to collect and process data for ultrasound measurements. (Image courtesy of Bay Area Sonic Solutions)

“In most ultrasound labs, MATLAB is the tool people learn first,” Pawashe describes. “The signal processing, image processing, visualization, and simulation tools are all there.”

MATLAB sits at the core of her measurement and analysis workflow. Raw electrical signals captured by hydrophones are transformed using calibration data to reconstruct acoustic pressure waveforms. From there, parameters such as peak pressures, integrals, and spatial field maps that describe how sound propagates through space are calculated. Once the measurement system is properly aligned, MATLAB enables repeatable signal processing and visualization, helping turn raw data into insight.

“As a solo founder, I wanted a tool [MATLAB] I already trusted and one that has strong support behind it.” – Leela Pawashe, Founder at Bay Area Sonic Solutions.

A Founder Perspective Grounded in Values

Starting her own company was a deliberate decision for Pawashe. After years in high-growth environments, she wanted to work on problems she cared about while building something more sustainable.

Her guiding principles are simple. Every problem has a solution, and people matter. She brings that mindset to every engagement, recognizing the effort it takes for teams to reach the point where they ask for help and meeting them with respect and curiosity.

Looking Ahead

Bay Area Sonic Solutions is launching its services to give more teams access to early ultrasound characterization without the burden of building their own labs. “I’m excited to give people the feedback they need earlier, so they can make the best devices they can,” Pawashe concludes. “That is what this is all about.”

For Leela Pawashe, measurement is not just about data. It is about helping teams learn faster, reduce risk, and move forward with confidence.

Learn more about Bay Area Sonic Solutions: https://www.bayareasonicsolutions.com 

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

Discover MathWorks Agentic AI Solutions on MathWorks.com

The era of agentic AI workflows has arrived and is here to stay. Each week, a growing number of startups across all industries are incorporating tools like Claude Code and Codex into their existing workflows. At the same time, investors are showing growing interest in startups that utilize agentic AI to improve their products and business models. Despite this momentum, there is not yet a clear, proven method for startups to use agentic AI when building technology based on an engineered system.

For engineering-focused startups, the lack of existing resources makes it significantly harder for them to adopt this new technology. Between tight deadlines and the need to build high-quality technologies, startups need clear ways to use agentic AI so they can move quickly with confidence. This post aims to address that need.

While there is not yet an established approach for applying agentic AI in engineering workflows, early use cases point to its potential. In combination with MATLAB and Simulink, teams are already using it to accelerate system-level modeling, testing, and simulation. These examples highlight where this approach can improve product development and provide a starting point for teams looking to adopt it.

Agentic AI in Practice: Use Cases with MATLAB and Simulink

To get the most out of agentic AI when building their engineering technologies, startups can pair their AI agent with MATLAB and Simulink to rapidly build digital twins of their technology as part of a model-based design workflow. By adopting this approach, startups will be able to quickly build strong digital prototypes of their technology before entering production. Specific opportunities to accelerate product development with agentic AI include:

Model Formulation

• Startups can prompt their AI agent to build an initial virtual model of an engineered product in MATLAB and Simulink, whether that is a drone, medical device, or battery system. To ensure the AI agent builds a strong model, startups will need to take the time to detail a specific prompt for the AI agent that specifies the project plan and requirements. The AI Agent then creates an initial model in MATLAB and Simulink based on the prompt. This is opposed to having engineers build the virtual model themselves, which can be time-consuming.

Unit Testing

• Similar to the model formulation approach, a startup can instruct an AI agent to simulate and test its product under different conditions. By prompting the AI agent to develop and run tests rather than doing it by hand, a startup will further speed up the development process, allowing engineers to focus more on analyzing test results rather than developing the tests themselves.

Debugging

• Using agentic AI with MATLAB and Simulink, startups can expedite the debugging process. Instead of manually searching for bugs, engineers can prompt their AI agent to identify and fix bugs in their models. When paired with a manual debugging process, this approach also increases an engineer’s confidence in the quality of the virtual model, as it has two ways to look for bugs.

Generating Internal Documentation

• As startups grow and more engineers join their team, they will need to have rich documentation of their past work to more easily capture and replicate their work. This is one area where agentic AI shines. By instructing the AI agent to review the session memory and capture the workflow, a startup can generate documentation in minutes rather than hours. This saves the engineering team time with strong, easy-to-understand documentation that can be shared internally.

A great example of this agentic AI-driven approach in the real-world can be seen in Lucid Motors’ presentation of their agentic AI workflows at the MathWorks Automotive Conference in April 2026.

Agentic AI, MATLAB, and Simulink: Accelerating Engineering Product Development

These examples highlight a clear finding: combining agentic AI with MATLAB and Simulink enables teams to build more quickly with greater confidence.

Startups can now direct AI agents to build and iterate on digital models more quickly than their engineering teams could on their own. With MATLAB and Simulink, those same workflows can extend into testing and debugging using mathematically grounded tools. As the technology continues to evolve, new opportunities are likely to emerge for improving how engineers build and validate complex systems with agentic AI.

To support this shift, MathWorks released the MATLAB Agentic Toolkit and Simulink Agentic Toolkit, which connect AI agents with MATLAB and Simulink.

With these toolkits, MATLAB and Simulink can serve as a trusted, proven engineering layer alongside emerging AI workflows, helping teams maintain rigor as they move faster.

Through MathWorks Accelerator Program, startups at partnered accelerators and incubators can use MATLAB and Simulink at no cost for one year. 

This is where conventional calibration approaches start to fail.

The Fragile Art of Modeling Volatility

The drift term, which captures an asset’s long-term direction, is rigidly set by no arbitrage conditions. Volatility is not. “Volatility is far more challenging,” explains Dr. Benjamin Joseph, Quantitative Researcher and Lead Core App Developer at genOTC. “Current models rely on simplifying parametric assumptions you wouldn’t necessarily want to impose; they are fragile and unstable. When these assumptions break, your model will struggle to replicate what the market is telling you.”

Most institutions rely on parametric families such as SVI or SABR that impose rigid structural assumptions on the surface or maintain in-house local volatility fits that require continuous recalibration and oversight. Both approaches can break under regime shifts, introduce arbitrage, and leave desks exposed to model risk, hedging error, and costly mispricing.

This persistent and expensive challenge has created room for a new generation of calibration technology. genOTC is reformulating the calibration problem from first principles, using optimal transport theory to produce arbitrage-free local volatility surfaces consistent with observed market data.

genOTC is not building another analytics dashboard or trading platform. It is building an arbitrage-free local volatility modeling and pricing engine, delivered as an AI-native platform built on optimal transport. Expressing calibration as a convex optimal transport problem gives a unique solution that matches market data and produces a smooth local volatility model suitable for pricing path-dependent exotics.

However, behind this apparent simplicity lies a strong mathematical challenge, the one that genOTC is tackling.

Building a Platform to Serve the Industry

Banks pricing path-dependent exotics such as autocallables, hedge funds developing systematic options strategies, and independent price verification teams all face the same requirement. They need models that are consistent with market prices. genOTC’s platform is built to deliver exactly that.

Users can upload their own data or use data sourced by genOTC, and the system generates an arbitrage‑free local volatility model using the company’s model‑free, optimal‑transport‑based approach. As Joseph describes, their methodology “works directly on the market data using the language of optimal transport.” Because the framework imposes no parametric form on the surface, the resulting model is arbitrage-free by construction and applies across asset classes without bespoke reformulation.

The genOTC app interface showing GOOGL (Image courtesy of genOTC)

Once calibrated, the platform becomes a workspace for analysis. Users can inspect implied and local volatility surfaces, evaluate how well the model matches input data, and understand its behavior through intuitive visualizations. From there, the calibrated model can be exported into pricing libraries for solving PDEs or running Monte Carlo simulations, which are essential tasks for fair pricing of over-the-counter derivatives. “Because you can’t just observe the market price [of an exotic], it’s important that you have a well-calibrated model that can tell you a fair price,” Joseph notes, underscoring the value of a reliable calibration engine.

For teams building automated workflows, the same functionality is available via an API, enabling scripted calibration and seamless integration with existing systems. The result is a workflow engineered for quants but accessible to anyone who relies on accurate models for financial decision‑making.

MATLAB Accelerates FinTech Development

Behind this workflow is a computationally intensive numerical engine. To build it, genOTC relies heavily on MathWorks tools. Joseph says, “We use optimal transport and PDE-driven techniques to create a local volatility model consistent with some given market data. Therefore, one of the key ingredients of our methodology is solving many PDEs numerically.” To achieve these results, the team relies critically on efficient linear algebra solvers, heavily uses vectorization capability, two things that MATLAB does very well.

“The ease of development with MATLAB cannot be overstated. The ability to quickly create toy experiments and visualize the results using MATLAB’s market-leading plotting software has greatly sped up the development of our algorithm.” – Benjamin Joseph, Quantitative Researcher and Lead Core App Developer, genOTC

The team uses MATLAB throughout their development workflow. MATLAB is used to write and test functions, plot three-dimensional volatility surfaces, run experiments, and iterate at a pace suited for research-heavy development. The Financial Toolbox and Optimization Toolbox are used as foundations for common financial calculations, including validation and regression tasks. When they’re ready to deploy, they compile the MATLAB code using MATLAB Compiler, package it into a Docker container with MATLAB Runtime, and run it on AWS EC2.

Visualization of META Local Volatility Surface – an example of MATLAB’s 3D plotting capabilities (Image courtesy of genOTC)

The team has drawn on multiple MathWorks resources throughout development. Engineering support and the MATLAB Answers forum have resolved implementation questions quickly, while the MATLAB Profiler has helped identify bottlenecks and improve the algorithm’s performance on computationally complex operations. “The addition of MATLAB Copilot has enhanced our workflow, with most routine questions rapidly answered,” says Joseph.

As genOTC’s team continues to grow, they anticipate expanding their use of MathWorks software, adding licenses to support both the quant and software development teams. Unlike traditional providers in the space, limited to providing services during business hours, genOTC’s AI-native platform will give market participants 24/7/365 access to a full workbench of services as a one-stop-shop for options, including: calibration-as-a-service, pricing, backtesting, and quant libraries. “With robust software, responsive support, and tools like MATLAB Copilot, MathWorks has been and will continue to be instrumental in helping our team streamline workflows and deliver more robust and reliable insights to market participants,” concludes Joseph.

Expanding Across Asset Classes and Users

genOTC has already calibrated models to equity index markets with European-style options, single-name equities with American-style options, and cryptocurrency markets. FX is currently in benchmark testing, with commodities and fixed income next in line. Despite the diversity of these markets, the underlying algorithm remains unchanged. What shifts is the data environment around it, not the modeling framework.

genOTC recently reached a milestone with the fall 2025 launch of an early version of their calibration tool. They are now expanding their AI-native workbench to include a pricer, backtesting capabilities, and a quant library, which are already available to select clients through co-development. The team is also broadening asset class coverage and partnerships while preparing for a Series A fundraise.

A Team Driven by Mathematics and Momentum

For Joseph, the work is deeply personal. His PhD work was about the theoretical foundations of calibration by Optimal Transport. genOTC allows him to explore the next question: how do we make it work every day, for real customers, in real markets?

The startup’s team of quantitative researchers is tackling open questions at the intersection of optimal transport and mathematical finance while delivering production software. The bar is high, and the pace reflects a group focused on translating rigorous research into tools desks can actually rely on.

genOTC is bringing a mathematically grounded, disruptive approach to one of the most persistent problems in quantitative finance. Paired with the right engineering tools, rigorous research reaches production, and market participants get pricing models they can rely on.

Agate Sensors is changing that with the world’s first fully software-defined hyperspectral chip that brings lab-grade spectral intelligence to an ultra-compact, mass-manufacturable form factor. By shifting intelligence from rigid, costly hardware to software, the team is making what was once inaccessible far more accessible.

“By bringing lab-grade hyperspectral capabilities to an ultra-compact, fully software-controlled chip, we are redefining spectral measurement and defining a new category of capabilities that simply did not exist before,” says Mikael Westerlund, Chief Business Officer and Co‑founder of Agate Sensors.

Breaking the limits of conventional sensors

Most sensors used in consumer and industrial devices today are built around fixed optical architectures that rely on filters or specialized optics to separate incoming signals before detection. While effective for traditional imaging, they impose hard trade-offs in terms of size, cost, and flexibility.

Agate Sensors takes a fundamentally different approach. “We are not using any filters or diffractive elements to separate wavelengths before detection,” explains Westerlund. “Because we use all available signal energy, the signal-to-noise ratio at the detector level is dramatically better. And without filters, the platform becomes inherently smaller, cheaper to manufacture, and scalable in ways conventional architectures cannot match.”

By eliminating the need for specialized optics, Agate Sensors enables a solid‑state sensing platform that is compact, robust, and highly programmable. Instead of designing a new sensor for every application, device manufacturers can adapt the same platform through software, shifting sensing behavior as requirements change.

This shift is especially important because innovation in conventional sensing has largely plateaued. As Westerlund notes, traditional imaging systems have focused on incremental improvements, such as higher pixel counts, rather than fundamentally new capabilities. “That development has stagnated totally,” he says. “It’s been years since we’ve seen something really new on these devices.”

By moving innovation into software, teams can explore new applications without re‑engineering hardware, reducing development time and lowering the barrier to experimentation.

As a result, a single sensing platform can support a wide range of use cases, from health monitoring and material identification to machine vision and environmental sensing.

From raw data to real‑time intelligence

At the heart of Agate Sensors’ platform is the ability to extract meaningful insight from rich spectral data. “Spectral data is an underutilized natural resource today because current technology is big, bulky, expensive, and impossible to miniaturize,” says Westerlund. “By miniaturizing this technology and making it affordable, it can be integrated into devices like mobile phones, wearables, cars, drones, and even satellites.”

This is exactly what Agate Sensors has achieved by enabling spectral data to be captured and interpreted in real time across many domains.

In wearables, for example, hyperspectral sensing could analyze biochemical signals without needles or test strips. In other applications, devices could identify materials or detect hazards in the field.

Agate Sensors develops technology to bring lab-grade hyperspectral capabilities to ultra-compact, fully software-controlled chips. (Image courtesy of Agate Sensors)

This approach also aligns naturally with artificial intelligence. “Current sensors are built to replicate how humans see the world,” Westerlund explains. “What we provide is much richer data that AI can use far more efficiently than humans ever could.”

Rather than producing images meant for people to look at, Agate Sensors’ platform generates measurement data designed for machines to analyze. Instead of being limited to red, green, and blue channels, devices gain access to deeper information that AI models can use to identify materials, detect signals, and make real-time decisions.

“This technology is really a match made in heaven between machine vision and AI‑driven applications,” Westerlund says. Turning this kind of data into reliable, real‑time intelligence depends not just on sensing, but on the algorithms that interpret it.

Using MATLAB to accelerate algorithm development

MATLAB plays a central role in Agate Sensors’ workflow, particularly during the algorithm development and validation phases, providing the accuracy and analytical depth needed to work with large, complex datasets.

As prototypes generate large volumes of complex data, MATLAB enables the team to explore, analyze, and iterate quickly. Agate Sensors relies on toolboxes such as Signal Processing, Image Processing, and Computer Vision to support these workflows. “MathWorks is a central tool for our algorithm development,” explains Tommi Leino, CEO and Co-founder of Agate Sensors. “We use MATLAB for spectral reconstruction, signal processing, and analyzing measurement data during development”.

MATLAB also helps bridge the gap between research and deployment. Once the algorithms are developed, the team uses MATLAB Coder to generate C code that runs on the CPU in their chip.

For a startup building custom chips, that continuity matters. “It definitely speeds up algorithm development because you have ready‑made toolsets and functions instead of coding everything from scratch,” Leino adds.

Moving fast from research to silicon

The core innovation behind Agate Sensors originated in academic research but turning that breakthrough into a scalable product requires both speed and technical rigor.

Like many deep‑tech startups, Agate Sensors operates under intense time pressure. Hardware development demands significant upfront investment, while customers expect rapid progress toward real‑time deployment. “Time is essential in the startup world,” says Leino. “We need to execute fast and still deliver high‑quality output for customers.”

Tommi Leino, CEO and Mikael Westerlund, CBO of Agate Sensors. (Image courtesy of Agate Sensors)

That urgency has shaped how the team approaches development. By focusing on software-defined sensing and a streamlined path from algorithms to silicon, Agate Sensors moves from research concepts to deployable hardware without sacrificing accuracy or flexibility.

Looking ahead

Agate Sensors is approaching a major milestone: receiving its first silicon back from the foundry. From there, the focus shifts to validation, customer proofs of concept, and preparing for mass production.

The longer‑term vision is clear. By embedding software-defined sensing into everyday devices, Agate Sensors aims to enable a new generation of intelligent systems that can perceive, classify, and understand the physical world in ways previously impossible.

For researchers and engineers considering a similar leap from academia to industry, the team’s advice reflects their own journey. “Close your eyes and jump,” concludes Westerlund. “You’ll never know if you don’t do it.”

Sometimes, the biggest breakthroughs come not from adding more hardware, but from rethinking where intelligence belongs.

The Challenge: Complexity Is Outpacing Traditional Methods

In Aerospace and Defense, mission complexity is growing faster than the ability to manage trade‑offs effectively. UAV operations are evolving from single-vehicle missions to multi-asset, coordinated systems operating in dynamic environments, while satellite architectures are shifting towards large constellations that require true system-of-systems thinking. At the same time, program timelines are shrinking, even as expectations for performance, resilience, and interoperability continue to rise.

The fundamental question facing engineering teams is no longer “Can we build it?” It is increasingly becoming, “Can we make the right decisions early enough?”

This is where Mission Engineering plays a critical role. It helps teams connect requirements, architecture, analysis, and verification to enable better decisions earlier in the lifecycle.

Mission Engineering: Enabling Better Decisions, Earlier

Mission Engineering focuses on evaluating systems in the context of real mission outcomes, rather than isolated component performance. By leveraging Model‑Based and digital engineering approaches, teams can:

→  Explore design alternatives quickly

→  Analyze trade‑offs before costly decisions are locked in

→  Validate concepts earlier using executable models

→  Reduce rework caused by late discovery of requirements or integration issues

As Aerospace systems grow more complex, engineering teams are rethinking how they design, analyze, and validate missions. Traditional component-level approaches are often insufficient for understanding system behavior, trade-offs, and risk at the mission level, especially early in the lifecycle.

Modern aerospace missions are no longer defined by individual platforms, they are defined by how systems interact across communications, sensing, modeling, autonomy, and AI to achieve mission‑level outcomes.

Across aerospace programs, a common set of patterns is emerging in how teams approach mission‑level modeling and decision‑making. The following themes reflect those patterns and highlight where mission engineering is having the greatest impact.

1. Fidelity Needs to Be Intentional

Not every decision requires high‑fidelity models. In fact, insisting on maximum fidelity at every stage can slow progress.

An effective mission engineering approach applies fidelity intentionally:

•  Use lower‑fidelity models for early exploration and rapid trade studies

•  Increase fidelity as design decisions narrow and risk areas become clearer

•  Define clear success criteria to understand when “good enough” is sufficient

This approach enables teams to move faster while still making informed decisions.

2. Digital Continuity Is Critical

Disconnected tools and handoffs remain a major cause of delays and rework in aerospace programs.

A strong Mission Engineering approach is built on digital continuity, connecting:

•  Requirements

•  System architecture

•  Analysis and simulation

•  Verification and validation

When these elements are digitally linked, teams can assess the impact of changes instantly, maintain traceability, and keep stakeholders aligned throughout the lifecycle.

3. Satellite Constellations Are Communication‑Driven

For satellite constellations, mission success is no longer defined by the performance of a single spacecraft.

The focus shifts to:

•  Coverage and revisit rates

•  Network resilience

•  End‑to‑end communication performance

•  Behavior under constraints such as link failures or congestion

Mission-level modeling allows teams to evaluate constellation behavior holistically, ensuring that system-level objectives are met, even under non-ideal conditions.

4. UAV Missions Demand Interoperability

Modern UAV missions involve multiple platforms, ground systems, and stakeholders. As a result, interoperability becomes a core design consideration.

This requires:

•  Alignment between mission requirements and system architecture

•  Shared models that span disciplines and organizations

•  Verification strategies that reflect operational realities, not just nominal cases

Mission Engineering helps ensure that all elements of a UAV ecosystem work together as intended.

5. Resilience Matters More Than Nominal Performance

Optimizing solely for ideal conditions is no longer sufficient for complex aerospace missions.

Teams need to evaluate system behavior under disruption, including:

•  Degraded communications

•  Asset loss or failures

•  Environmental uncertainty

•  Adversarial or contested scenarios

Mission‑level analysis helps uncover vulnerabilities early and guides more resilient system designs.

Looking Ahead

For aerospace startups and innovators, Mission Engineering is quickly becoming a competitive advantage. By focusing on early insight, intentional fidelity, and connected digital workflows, teams can reduce risk, accelerate development, and deliver systems that perform not only on paper but in real missions.

As mission complexity continues to increase, the ability to decide early, model wisely, and design for resilience will define the next generation of aerospace innovation.

Innovating Mission Engineering for Tomorrow Webinar Recordings

If you want to explore these ideas in more depth, recordings from the Innovating Mission Engineering for Tomorrow series are available here:

• Engineering UAV Missions: Digital Tools for Complex Scenarios
• Mission Engineering for Satellite Constellations

Each session includes practical examples, workflows, and demonstrations using MATLAB® and Simulink®, showing how digital tools can support mission‑level decision‑making from concept through validation.

This was the reality facing Raptee.HV, an India-based startup developing the country’s first high-voltage electric motorcycle. While high-voltage architecture has become common in electric cars, it is not the standard in the two-wheeler market. There is no established ecosystem to build upon, no off-the-shelf components, no reference designs, and no proven playbook.

The team did what many deep‑tech startups must do: they started from the ground up.

Designing a Motorcycle From Scratch

Raptee.HV’s goal is to deliver a technologically advanced motorcycle that elevates the everyday commuting experience. Pursuing a high‑voltage architecture means the team has to design and validate every major subsystem themselves. From the powertrain and battery pack to the motor controller and suspension, each component is engineered, modeled, and tested in-house.

For a small team working under constrained timelines and resources, relying on traditional build‑and‑test cycles is not an option.

Moving Development into the Virtual World

To keep pace, Raptee.HV has adopted a Model-Based Design workflow using MATLAB and Simulink. By shifting early development into a virtual environment, the team can explore ideas, test assumptions, and uncover issues long before hardware is involved.

Using Simulink, engineers create detailed digital models of the motorcycle’s key systems. These models allow them to experiment and iterate before machining parts.

Just as importantly, modeling helps the team manage complexity. High‑voltage systems demand tight coordination between controls, power electronics, and energy storage. MATLAB enables engineers to develop and validate complex algorithms for the battery pack and motor controller while continuously evaluating efficiency and performance.

The result is faster iterations and better design decisions earlier in the process.

One Engineer, One Workflow: Traction Inverter Development

One clear example of this approach is the development of the motorcycle’s traction inverter.

In a traditional setup, this process would involve multiple handoffs. Control engineers, embedded programmers, and test engineers each working in different tools. Instead, MATLAB and Simulink enable a single, end-to-end workflow. One engineer designs schematics, models system behavior, generates code, and deploys it directly to the target hardware for testing on the bike.

With Embedded Coder, Raptee.HV generates production-ready C code straight from their Simulink models. This approach eliminates delays and reduces the risk of translation errors between design and implementation.

Faster Development, Higher Confidence

For Raptee.HV, Model-Based Design isn’t just about speed; it is about confidence.

By identifying and fixing issues in simulation, the team has reduced overall development time. They can analyze control‑loop stability, run Hardware‑in‑the‑Loop (HIL) tests, and validate system behavior across operating conditions before those systems ever reach customers’ hands.

High-fidelity models also play an unexpected role beyond engineering. For the startup, demonstrating validated system behavior helps the team clearly communicate technical progress to investors during early development.

As Phunith Kumar V, Co‑founder at Raptee.HV, concludes, “I believe that the pace of innovation is gated by the pace of iteration. What MATLAB helps us to do is iterate fast, even without going to hardware, which helps us reach new levels of product development.”

For a startup building something the market has not seen before, that ability to iterate quickly and with confidence can make all the difference.

Hear more from the Raptee.HV team:

Learn more about Raptee.HV: https://www.rapteehv.com

Read more on how Raptee.HV uses Model-Based Design: https://www.mathworks.com/company/mathworks-stories/designing-indias-first-high-voltage-electric-motorcycle-with-model-based-design-and-code-generation.html 

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

The Problem: Data Silos Hold Engineers Back

Engineering organizations generate tons of data, but much of it is scattered across laptops, servers, test rigs, and disconnected tools. Mike Rosam, CEO of Quix, explains, “That really prevents engineers from using more modern analytical techniques like data science, machine learning, and AI at scale.” The result is an undesirable, slower time to market, lower product quality, and missed opportunities for automation.

Organizations trying to overcome this have a few decisions. They can build a custom in-house system, which can be expensive and complex to maintain, hire consultants for a digital transformation, again costly, or buy an enterprise platform, requiring customization and long deployment timelines. “Every R&D organization in the world has its unique processes,” Rosam notes. “It’s really hard to buy a standardized product that you can just purchase like a CRM for marketing data.”

The Solution: Quix – A Platform Built for Engineers

Quix flips the script with a developer platform designed for engineers. “We’re a Python-native development environment, so engineers can build their own workflows in languages they already use,” Rosam describes. “There’s no DevOps. Engineers write Python scripts, deploy them, and they’re up and running.”

The platform handles two major jobs:

1. Data Ingestion: Quix makes it easy to build data pipelines from test rigs, labs, and simulation tools, normalizing and enriching data for analytics. Engineers customize connectors using AI‑assisted code generation, then route data into a centralized warehouse. Metadata from configuration systems is automatically merged, allowing downstream tools to consume analytics-ready datasets. Mechanical and test engineers can set up pipelines themselves without IT tickets or delays.
2. Analysis and Automation: Once data is centralized, engineers can pull it into the tool they prefer. From MATLAB and Simulink to Jupyter Notebooks or custom tooling, the open structure enables hybrid toolchains rather than locking teams into one environment. A powerful use case is to automate event-driven analysis, triggering simulations, validation routines, or model-based workflows as soon as new test data arrives. Rosam explains, “We’re really trying to automate all of those steps in the engineering workflow and let engineers build their own workflows.”

“A big differentiator for Quix is it’s very open,” Rosam notes. “We can get data from any R&D tool, any physical system, into a consolidated cloud and let engineers pull the data into any tool.”

Seamless Integration with MATLAB and Simulink

Quix’s platform is deeply integrated with MATLAB and Simulink. “We use MathWorks tools every day to help our customers solve problems,” Rosam says. “The integrations we’ve built make it easy for engineers to acquire data from simulations and serve models in the cloud.” Engineers can use a Simulink block to stream data from Simulink models directly into Quix Cloud. They can run MATLAB and Simulink models inside Quix against live or historical data streams. Or parameterize models dynamically for real-time digital twin applications.

The Quix.IO platform seamlessly integrates data into Simulink. (Image courtesy of Quix)

Quix’s approach is already penetrating industries from motorsport to manufacturing. For example, a Formula One team uses the platform to run digital twin models in real time as the car is driving. When the car changes the front wing angle, engineers update the parameter, and the digital model running in Quix adjusts instantly. This keeps the virtual system aligned with reality, which is critical for verification and validation.

“We work in a very practical way,” Rosam emphasizes. “We identify a key bottleneck in the R&D process and fix that quickly, sometimes within a month or two. This isn’t a years-long, million-euro digital transformation. It’s pragmatic, high-impact problem-solving.” This model has helped customers accelerate simulation workflows, improve validation cycles, and close data loops between physical and digital environments.

Partnering with MathWorks Startup Program

For Quix, MathWorks Startup Program has been a foundational partner. The startup joined the program early to obtain access to MATLAB to help a customer. This quickly grew into a much more collaborative partnership. “Startups are cash-constrained, so the Startup Suites is a no-brainer,” Rosam shares. “But the support has been unrivalled. MathWorks went the extra mile, from account support, engineering support, even marketing support. We haven’t seen this level of support from other tech vendors.”

For a lean startup managing product development, customer success, and operations, this support saves both time and capital.

What’s Next for Quix

Quix is growing rapidly. They are looking forward to opening new offices in Prague and London. The team is hiring, launching new initiatives, and looking ahead to future funding rounds. Their mission remains the same, to remove data friction so engineers can focus on engineering. Rosam concludes, “When a customer says, ‘You changed the way we work,’ that’s the reward. We want to deliver that every day.”

Learn more about Quix: https://quix.io/

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

This is the backstory behind the startup company weg//weiser. While working in a laboratory focusing on drive technology, some friends, Christian Klöffer and Philipp Degel, faced a consistent problem. “Testing new motors without the necessary information was always a major headache,” recalls Philipp Degel, CEO of weg//weiser. “We started looking into the possibility of automatically measuring electric motors, and eventually decided to build a company around solving this problem.” The years of pain were inspiration to come up with a solution for fellow engineers.

Electric vehicles (EVs) have become a backbone of the energy transition. The core of every EV is a complex electric motor. Getting these motors from the lab to the road is a challenging process. For engineers, testing and commissioning new motor designs can be a time-consuming, costly, and expertise-heavy process. Weg//weiser aims to change that with a system that automates and accelerates electric motor characterization, delivering test results faster with exceptional performance.

The Problem: Electric Motor Testing is Slow, Expensive, and Complex

The global shift to electromobility is reshaping how people and goods are moved. As new types of electric machines are developed, testing and measuring of these motors’ performance remains a critical bottleneck. Information is required to set operating points for certain conditions, such as the torque to be achieved under a specific speed, voltage, and temperature. This leads to the question of how the necessary complex information should be obtained. Companies are suddenly confronted with new, highly technical tasks.

The current situation is usually such that internally developed solutions have to be constantly modified and adapted to new requirements, or are underutilized. A lack of automation and complex integration increases the time required for commissioning, leading to slower innovation and rising costs for the use of expensive laboratories over a longer period of time

The Solution: weg//weiser’s Automated Universal Motor Test System

Weg//weiser’s solution is a universal test system for all types of electric motors. The software provides a fully automated evaluation of all measurement data across the entire speed range, deriving all relevant control parameters for optimal and safe motor operation. The flexible environment can be integrated into existing testbed systems, while allowing for customer-specific adaptations to meet tailored process solutions and expandable to future needs.

“Our mission is to empower our customers to complete their tests in record time with exceptional performance,” says Degel. “We want anyone to operate and analyze any electrical machine without needing expert-level knowledge.” To face testing complexity for operators, the platform incorporates a simple interface with “one-click” for commissioning, characterization, and evaluation of electric machines.

As Degel puts it, “Automated measurement routines, combined with detailed data evaluation, achieve the commissioning of electric motors in the shortest time possible.” This approach saves customers significant time and money when bringing new products to market, while delivering reliable, repeatable results.

weg//weiser’s operator interface for testing and commissioning of engines. (Image courtesy of weg//weiser)

Accelerating Innovation with MATLAB

A key part of weg//weiser’s rapid development of their platform was integrating MATLAB into their engineering foundation. “As an engineer, you often have the false perception that you could solve the problem better yourself. You must learn that nobody pays you money to reinvent the wheel,” says Degel. “MATLAB gives us the opportunity to focus our energy and time on innovation.”

The company developed sophisticated algorithms for evaluating measurement data in real-time with MATLAB and Simulink. To streamline the transition from development to deployment, weg//weiser uses Simulink Coder to generate code for their dSPACE system. Finally, the team leverages MATLAB Compiler to create a front-end for their parameterization and data evaluation tools. Stateflow automates even the most complex test routines.

Degel points out that using MATLAB and Simulink enabled weg//weiser to deliver their new platform to market faster and with reduced R&D costs by an estimated 50%. He adds, “Thanks to MathWorks tools, we as a small company can still develop very quickly and generate a high output of innovation. As we must be technically on a par with global corporations, this is one of the keys to the [startup] company’s success.”

Lessons for Founders

Weg//weiser’s journey hasn’t been without unique challenges. As an engineer turned CEO, Degel realizes the need to keep the overall product and customer in mind without getting lost in the technical details of the solution. His advice for other founders comes from his own experience. “Never stop learning and always meet people with an open mind. Treating customers with respect and trust is the most important thing. If the customer knows they can rely on us, you build long-term and reliable business relationships,” concludes Degel.

By making electric motor testing faster, easier, and more accessible, weg//weiser is helping drive the future of electromobility.

Learn more about weg//weiser: https://future-of-tomorrow.com/

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html