AI Built for
the Real World

ModelCat is an AI-powered platform that builds and optimizes production AI systems for real-world hardware environments.

ModelCat was created to solve a fundamental problem in modern AI: models perform impressively in the lab, but too often fail in production once real system constraints are involved.

As AI moves out of the cloud and into devices, factories, vehicles, and edge solutions, success is no longer defined by benchmarks alone. It’s defined by whether AI systems operate reliably, efficiently, and predictably under real-world deployment constraints

That gap between promise and production is where ModelCat exists.

What
ModelCat
Is

ModelCat replaces fragmented AI development workflows with an AI-driven, system-level approach that continuously evaluates requirements, explores tradeoffs, and guides optimization decisions throughout the AI development process.

Instead of manually selecting architectures, tuning models, and validating performance across disconnected tools, ModelCat uses AI to orchestrate these activities within a single, constraint-driven workflow.

The result is production AI built faster, validated on real hardware, and designed for where it actually runs. Built to operate within enterprise environments, ModelCat integrates seamlessly with existing software development lifecycles, governance frameworks, and security policies while providing a structured, measurable path from development to deployment.

OUR APPROACH

AI That Builds Production AI Systems

ModelCat is AI that builds AI.

Instead of relying on manual tuning or traditional software workflows, ModelCat uses AI to explore thousands of model and hardware tradeoffs simultaneously. It solves optimization problems that can’t realistically be solved by software alone.

Our platform autonomously generates, optimizes, and validates production AI systems based on real hardware behavior—not abstract assumptions. ModelCat continuously evaluates tradeoffs across performance, power, memory, and reliability to produce models that actually work where they’re deployed.

This approach flips the traditional workflow on its head:

  • Hardware constraints are design inputs, not deployment surprises
  • Confidence comes from validation, not guesswork
  • Speed doesn’t come at the cost of control

The Result

Production AI that is faster, more reliable, and grounded in real-world deployment conditions.

Our Mission

As AI moves deeper into products, infrastructure, and everyday systems, reliability matters more than novelty.
ModelCat’s mission is simple:
To make production AI model development more accessible, predictable, and reliable without sacrificing control or trust.

Part of a Growing Ecosystem

ModelCat works closely with leading hardware and platform partners to ensure seamless deployment across real-world systems.

Our collaboration with NXP Semiconductors enables customers to generate production-ready models for NXP devices in days—not months—accelerating time to market while reducing deployment risk.

Built by People Who’ve Lived the Problem

ModelCat was founded by engineers and leaders who have spent their careers building and deploying complex systems with millions of endpoints —and who have experienced firsthand how difficult the final mile of AI deployment can be.

This isn’t a theoretical problem for us. It’s one we’ve worked through across real products, real customers, and real hardware.

Evan Petridis

CEO

Evan is an experienced technology executive and serial entrepreneur with a track record of successful exits. Bringing deep expertise in AI, product development, and systems engineering, he founded ModelCat to address the growing disconnect between AI innovation and real-world deployment—building a platform that removes friction instead of adding complexity.

As co-founder of Atmosphere Networks (acquired by Ditech) and Chief System Architect at Enlighted (acquired by Siemens), he was responsible for delivering large-scale, AI-enabled solutions that leveraged data from millions of sensors globally. Evan was also a hardware engineering leader at Cisco Systems where he played a major role in developing some of Cisco’s most successful products.

Jeremi Wojcicki, PhD

CTO

As co-founder of Atmosphere Networks (acquired by Ditech) and Chief System Architect at Enlighted (acquired by Siemens), he was responsible for delivering large-scale, AI-enabled solutions that leveraged data from millions of sensors globally. Evan was also a hardware engineering leader at Cisco Systems where he played a major role in developing some of Cisco’s most successful products.

Jon Gettinger

CRO

Jon brings decades of experience in enterprise technology sales and marketing, particularly in next-generation platforms and developer-focused tools, driving clarity, credibility, adoption, and revenue for industry-leading companies. As a seasoned GTM startup executive, he specializes in helping innovative technologies reach adoption by translating complex technical capabilities into clear market narratives. 

Jon has repeatedly helped scale emerging technology companies from early stages to successful outcomes. Most recently, he led Pipe17 from inception to a recognized leader in order operations in just four years, building on a track record that includes companies such as Pure Software, Fortify Software, and Xamarin.

What We Believe

Our values reflect how real systems get built and what it takes to ship AI that works.

Bold

We challenge assumptions about how AI should be built and deployed, and we’re not afraid to rethink entrenched workflows. ModelCat applies AI itself to solve the complex optimization problems behind production AI model development, exploring thousands of design possibilities that would be impossible to evaluate manually.

Fluid

AI is evolving at an unprecedented pace. New models, chips, and deployment platforms appear constantly. We stay on top of that complexity so our customers don’t have to.

United

Hardware, software, and AI are part of the same system. We build with that reality in mind.

When these elements come together, entirely new classes of intelligent systems become possible—solutions that would have been impossible just a few years ago.

Judicious

We value production confidence over hype, and decisions grounded in evidence over speculation. We follow the old carpenter’s saying: “Measure twice and cut once.”

Where We’re Headed

BOOK a demo

As AI becomes embedded in everyday systems, the teams that succeed won’t be the ones with the biggest models. They’ll be the ones who can build and deploy production AI systems reliably, repeatedly, and with confidence

ModelCat exists to make that future possible.

See how ModelCat builds production AI on real hardware.