AI is transforming software development, but it's also creating a correctness crisis. As AI generates code at unprecedented scale, we apply formal methods to verify software behavior before it reaches production —giving companies the confidence, compliance, and speed to ship what's proven, not assumed.
Code at AI Scale.
Correctness Without Compromise.
Ship What’s Proven, Not Assumed.
∙ Reduce Rework – Eliminate manual regression testing that consumes 30–40% of engineering cycles across modernization, porting, and AI-assisted development projects.
∙ Prevent Production Incidents – Prove behavioral equivalence before AI-generated, refactored, or ported code reaches production — in any industry, at any scale.
∙ Ship with Confidence – Give engineering leaders mathematical certainty, not just test coverage, before every significant code transformation.
Every software organization is now a code transformation organization. AI coding tools are accelerating how teams write net-new code, refactor legacy systems, and port across languages, platforms, and architectures at a pace and scale no human review process was designed to handle.
But the validation layer hasn’t kept up. Tests sample a fraction of possible behavior. Code reviews miss what they can’t anticipate. Static analysis flags syntax, not semantics. No existing tool can prove that transformed code behaves identically to the original across all inputs whether that code was written by a developer, an AI agent, or both.
That gap creates a universal risk: behavioral regressions that are invisible until they’re expensive. A financial services firm shipping AI-refactored transaction logic. A healthcare platform porting to a new cloud runtime. An industrial manufacturer modernizing embedded control systems. A SaaS company accelerating feature velocity with AI-generated code. The blast radius differs, but the underlying problem is identical.
The result is a forced tradeoff every engineering leader faces: ship fast with unknown behavioral risk, or slow down and test more, knowing you still can’t prove correctness. More AI tooling makes this tradeoff worse, not better.
The market needs a verification layer that fits real development workflows: automated, language-agnostic, and capable of returning either mathematical proof of equivalence or actionable counterexamples before risk compounds in production.
* Awarded a DARPA contract supporting 12 months of full-time commercialization effort following selection into DARPA’s Embedded Entrepreneur Initiative (EEI); working with Capital Factory and a DARPA Senior Commercialization Advisor (SCA) to accelerate FMToolkit go-to-market and customer traction.
* Selected for and completed MassChallenge’s Security & Resiliency program, expanding our commercialization network across government, enterprise, and investor ecosystems.
