Knowledge base
We ingest docs, tickets, decisions, incidents, feedback, and source code — then organize the context and surface the contradictions hiding between them.
Finds what the retired engineer knew
Code Atlas turns scattered software history — code, commits, tickets, chats, logs, and decisions — into living artifacts the humans who maintain software and the AI systems that generate it can both trust.
Code writers are everywhere. The context that keeps them safe is not. Code Atlas compiles your software's real history into artifacts a person can read and a model can act on.
We ingest docs, tickets, decisions, incidents, feedback, and source code — then organize the context and surface the contradictions hiding between them.
Documents become the source of intent; code remains the source of truth. Together with existing tests they compile into traceable, machine-readable context.
Differential validation proves the rewrite behaves like the original — line by line. The output is evidence, not a promise: the proof a bank can sign.
Generation got cheap overnight. Understanding, validating, and documenting did not — and that is where defects, distrust, and audit risk now collect.
The work AI creates — specifying, validating, documenting — is exactly the work nobody wants.
Five stages turn dispersed history into context you can act on — the missing control plane in AI software development.
Docs · tickets · decisions · incidents · feedback · source code.
Ingest content, organize context, and surface discrepancies.
Organizational memory plus a map of software functionality.
Docs as intent, code as truth — alongside existing code and tests.
Generate, refactor, and translate code against the spec.
One metric runs through every tier: validated KLOC. You pay only for proven output — failed translations cost you nothing.
The market doesn't need another code writer. It needs the context layer that keeps code writers safe — and the conditions for it just arrived.
Higher task throughput still creates larger PRs and heavier review. The constraint moved from writing code to specifying it.
Issue discovery is cheap now. Safe patching, validation, and auditability are the hard parts that remain.
C and C++ systems need a reliable map before any team can move critical paths to Rust with confidence.
Models can finally fuse code, tickets, chat, docs, and logs into cited, machine-readable artifacts.
Adoption starts with a single engineer and a free scan, and grows on the one thing security teams can't argue with: proof.
Published benchmarks anyone can re-run. Others claim AI modernization; we publish the proof.
The free scan finds what the retired engineer knew. In the POC, your engineers watch the harness verify the rewrite — line by line.
Security pulls Code Atlas from one team to the platform, where audit-readiness becomes a standard.
You pay per verified result. The system learns from every job, so quality compounds as volume grows.
Differential validation turns AI-written code into evidence a regulated business can sign off on. Start with a free scan of one repository — and see what your software already knows.