AI writes it fast. We prove it’s correct.
AI-accelerated software development from a team whose entire practice is verification.
Everyone can generate code quickly now. Almost nobody can tell you whether it’s right.
The problem with fast code
AI-generated code is plausible. It compiles. It reads well. It passes review by someone skimming. And it can be quietly, specifically wrong in ways that only surface under load, at a boundary condition, or in production.
This is a different failure mode from the one our industry built its practices around. Human-written bugs tend to look like bugs. Generated bugs look like working code, which means the ordinary defences — code review, a glance at the diff, “it looks fine” — are precisely the ones that fail.
Most development shops can offer nothing against this beyond a promise to be careful.
What we do about it
We use the same methods we use on systems where being wrong costs real money.
Specification first. We write down what the code is supposed to do, precisely enough to check against. This is the step that makes everything after it possible, and it is the step generated code skips by default.
Property-based testing. Not a handful of examples, but statements about what must always be true, checked against thousands of generated inputs. This is how you find the boundary case nobody thought to write a test for.
Types that carry the constraints. We work in languages where whole categories of error are unrepresentable rather than merely tested for — and we bring that discipline to codebases in languages that don’t enforce it.
Independent review by people who write this code for a living. Our verification practice audits systems handling real money. The same engineers, the same scrutiny.
What we build
AI-accelerated delivery. We build your software, using AI to move at the speed the tools now allow, with the verification layer that makes that speed safe to accept. You get the pace and you keep the assurance.
Verification of AI-generated codebases. If your team has already shipped a large volume of generated code and is uneasy about it, that unease is well-founded and it is fixable. We assess what you have, tell you where the real risk is concentrated, and prove our findings rather than assert them. This is a distinct engagement from building — see Verification and Auditing.
Trustworthy AI systems. Where the AI is the product rather than the tool — agent execution, connectors, safety perimeters, bounded action — we design and build the parts that must not fail.
Why us
We did not add verification to an AI practice. We added AI to a verification practice.
MLabs has spent years in Haskell, formal methods and smart contract auditing — building and checking systems where a subtle error is a financial event rather than a bug report. That is exactly the muscle the AI-accelerated market is missing, and we built it for entirely unrelated reasons, long before it was the useful thing to have.
We also maintain Plutarch, Lambda Buffers and CTL — open-source infrastructure other engineers depend on. That is the standard we hold our own output to.
Shipping generated code faster than you can check it?
That is a solvable problem, and it is the one we are built for. Tell us what you’re building or what you’ve built, and we’ll tell you honestly what we’d do about it.