Do you have too much AI code to review, or are you concerned about AI slop? We have the answer. It's called Vericoding.
Uncertainty to proof
Traditional unit tests and AI code reviews only check the paths you think of. They miss the microsecond race conditions, security vulnerabilities, and logic flaws that crash production.
You write the specification (the contract).
The LLM writes the code.
Our theorem prover mathematically guarantees the code meets your exact specification.
If it doesn't, it doesn't ship.
Concurrent systems are notoriously difficult to test. We provide mathematical proof that your asynchronous schedulers and multi-threaded environments are safe by construction.
We refine and compile specialised query execution and API routing layers to bypass bottlenecks.
Happy path testing for your security is unreliable. We formally verify your access control logic, mathematically proving that no edge-case combination of roles can bypass your security framework.
Our technology provides the greatest ROI in sectors where a single software defect results in critical failure:
Eliminating logic bugs in high-frequency trading.
Slashing compute overhead for SaaS platforms.
Guaranteeing deterministic safety for regulatory compliance.
Training
Vericoding training equips engineering teams in high-stakes environments to put formal verification in their CI/CD pipelines. Your engineers own the specification. The prover runs on the change. A failing proof does not merge.
Vericoding trainingThree open-source tools, one idea: the contract is the artifact you negotiate with the LLM, run as a test, and hand to the prover.
Specification-driven verification. Contracts — preconditions, postconditions, invariants, frame conditions, and exception exits — written as plain Python functions and organised by entry point. The contract language is the single source of semantic truth from which every test and proof artefact is derived.
Mechanised proof for vanilla Python. Contracts are plain assert statements — no imports, no decorators. A Coq + SMT pipeline proves properties; an LLM oracle handles the rest. Zero runtime overhead under python -O.
An MCP-based proof assistant for Rocq. Hash-addressed goals, coq-lsp-authoritative state, and parallel goal closure let frontier LLMs discharge real theorems — PCF type preservation for $0.06.
Vericoding Services
Models now write more code than you can review line by line. You specify what the program must do. We prove the implementation meets that specification, and where latency or compute cost is the problem, we replace it with a faster program that still does.
See the services
Software's core problem is knowing what it should do. AI-guided certified program refinement — vericoding — makes specification the artefact we tinker with, and lets AI guess program transformations that a theorem prover certifies.
From exam-gaming models to speciation and military embodiment — a map of AI risks, how we might assess them, and why mitigation is harder than it looks.
You do not have to go from zero to verified in one step. You can start with Gherkin scenarios, graduate to executable contracts with specsaver, and then bring in a theorem prover when you are ready. Contracts are the bridge.
Talk to us about Vericoding your infrastructure, or read how our open-source tools turn contracts into proof.