Your AI writes code. We prove it's the right code.
Predictable Code uses Lean 4 formal verification to prove that AI-generated code matches your specifications. Linters check style. Tests check behaviors you thought to test. We verify correctness across every repo and team.
ΛΛ ΛΛ ΛΛ ΛΛ ┌───────────────────────────────────── ΛΛ ΛΛ PREDICTABLE MACHINES [Interactive] ΛΛ ΛΛ version: nightly-443086f ΛΛ ΛΛ ──────┘ ΛΛ ΛΛ ΛΛ ΛΛ ┌── Status (Ctrl+H expand) ──────────────┐ │ │ │ [?] No specs — infer specs first │ ────────────────────────────────────────────────────────── ❯ Infer specifications from my codebase▌ ────────────────────────────────────────────────────────── Send (Enter) • Accept (→) • Ctrl+S • Quit (Ctrl+C)
Core Principles: The Problems We Solve & How We Solve Them
Four foundational principles that ensure AI-generated code meets production standards.
Shared Specification Context
Your requirements are encoded as formal specifications that every contributor works against. Not docs that can be skimmed. Machine-checked constraints that apply to every change, regardless of who or what wrote the code.
Every AI session starts from zero
Each AI assistant works from whatever fits in its prompt window. It doesn't know your architecture, your conventions, or what the last three PRs assumed. Across multiple developers and models, every file reflects a different understanding of the system.
Read moreValidate the generated code against global and local specs & requirements.
Continuous Verification
Every change is checked against your specs in real time, producing mathematical proofs of correctness. If something violates a requirement, you know immediately and you know exactly what broke.
AI-generated code that looks correct and isn't
AI produces code that reads well and quietly breaks assumptions elsewhere. Different models give different implementations of the same requirement. The more you ship, the more it compounds.
Read moreBidirectional Sync
Code, specs, and documentation are formally linked. Change your code, docs update. Change a requirement, non-compliant code gets flagged. Sync flows in every direction automatically.
Docs and code drift apart
Without a verified link between specs, docs, and code, every update is an assumption. Three things that look current, none provably consistent.
Audit-Ready Proofs
Cryptographically verifiable proofs connect requirements → implementation → verification. Complete traceability for high-stakes industries like finance and healthcare.
Compliance evidence that doesn't exist until someone asks for it
Today that evidence is assembled manually: screenshots, spreadsheets, review logs. It takes weeks, it's incomplete, and it can't keep pace with AI-generated code.
Works where your team already works
Run verification locally from the terminal or let the GitHub App catch spec divergences on every pull request — automatically.
CLI App
Verify your changes against specs before you commit. Runs in seconds locally — no server, no setup beyond a single install.
GitHub App
Every pull request is automatically checked against your specs as a native GitHub status check. Divergences block merges before they reach production.
github.com / acme / api · PR #142REQ-0001 · User authentication flow
Matches spec
REQ-0002 · Token expiry handling
Matches spec
REQ-0007 · Rate limit header format
Diverges from spec
REQ-0009 · Audit log format
Running…
Beta in progress
We're building the beta and selecting the first round of engineering teams to try it.
If your team ships AI-generated code and needs it to stay aligned with specs, we want to hear from you.
What's next
Private Beta
Early development partners, integrations, onboarding
Public Beta
Q2 2026
Expanded access, additional LLM support
General Availability
Q3 2026
Enterprise features, SLA guarantees
Frequently asked questions
Everything you need to know about Predictable Code.
Predictable Code is a verification layer for AI-generated code at enterprise scale. It converts your requirements, specs, and documentation into an always-on verification system that keeps AI-generated code aligned, consistent, and safe across every codebase and team.
No. Predictable Code works alongside your existing AI coding assistants — Claude Code, GitHub Copilot, Cursor, Codex, and others. It doesn't generate code itself; instead, it verifies that whatever code your AI assistant produces actually meets your requirements and specifications.
Every time code is generated or changed, Predictable Code automatically checks it against your formal requirements in real time. This happens in your IDE, in pull requests, and in your CI/CD pipeline — catching inconsistencies before they reach production, regardless of which LLM or developer wrote the code.
Linters check syntax and style. Tests check specific behaviors you've already thought to test. Predictable Code verifies that your code actually implements the requirements in your specs — catching the gap between what was intended and what was built. It also ensures consistency when multiple developers and multiple AI models are producing code across your organization.
In most teams, documentation and code drift apart over time. Bidirectional sync means that when code changes, your docs update automatically — and when specs are updated, Predictable Code flags code that no longer complies. Your documentation and implementation stay in lockstep.
For regulated industries like finance, healthcare, and aerospace, Predictable Code generates cryptographically verifiable proofs that connect requirements to implementation to verification. This gives compliance teams a complete, tamper-evident trail from spec to shipped code.
Predictable Code is built for any team shipping AI-generated code at scale, but it's especially valuable in regulated and safety-critical industries: financial services, healthcare, aerospace, safety-critical systems, streaming media, scientific research, and large open-source projects.
Predictable Code plugs into the tools you already use. It integrates with AI coding assistants (Claude Code, GitHub Copilot, Codex), platforms (GitHub, GitLab, CI/CD pipelines), and documentation tools (Confluence, Notion, Atlassian). No need to change your workflow — it adds a verification layer on top.
When multiple developers and AI assistants work across a codebase, context gets lost — each AI session starts fresh, and different team members have different understandings. Shared Team Context provides a unified source of truth for requirements, architecture decisions, and reasoning, so every developer and every AI assistant works from the same page.
Predictable Code is currently in private beta. Public beta is planned for Q2 2026, with general availability in Q3 2026. You can join the private beta today to get early access and help shape the product.
During the private beta, Predictable Code is free for accepted participants. Pricing for the public beta and general availability will be announced closer to those milestones. Join the beta to lock in early-adopter benefits.
Yes. Predictable Code is designed with enterprise security in mind. Your source code and specifications are processed securely and are never used to train models or shared with third parties. We follow industry best practices for data handling and access controls.
Still have questions?