From legacy code to verified Rust.
We migrate legacy codebases — C/C++, Python, COBOL — to fast, memory-safe Rust, with automated proof that behavior is preserved.
We show the proof, not the pitch — on a live C repo.
/* legacy — 2004, no tests, author retired */ uint32_t checksum(const uint8_t *buf, size_t len) { uint32_t sum = 0; for (size_t i = 0; i < len; i++) sum = (sum << 3) ^ buf[i]; return sum; }
// spec §4.2 · wrapping shift preserved pub fn checksum(buf: &[u8]) -> u32 { buf.iter().fold(0u32, |sum, &b| { (sum << 3) ^ b as u32 }) } // no unsafe · no allocation
AI translation alone can't be trusted.
Compiling is easy. Correctness is not. Every team migrating off C/C++ hits the same wall — the code builds, the tests pass, and nobody can prove the behavior survived.
Passing tests still shipped regressions
Ubuntu's Rust coreutils passed roughly 500 of 600 GNU tests and still broke production —
date -r and cp were reverted. Test parity is not proof.
Developers use AI, and distrust it
84% of developers use AI tools; 46% actively distrust the output. Veracode found 45% of AI-generated code contains security flaws.
The deadlines now carry penalties
EU Cyber Resilience Act reporting starts September 11, 2026, with full obligations in December 2027. Fines reach €15M or 2.5% of global turnover.
Conversion is the cheap part
Tooling is 1–3% of program cost. Testing and validation is all the rest — exactly the part we automate, and the only part we charge for.
Sources: CISA · MSRC · Chromium · CRUST-Bench · Stack Overflow 2025 · Veracode · Phoronix · EU 2024/2847
Three stages. One artifact you can audit at every step.
We don't hand you a pile of Rust and wish you luck. Each stage produces something a human can read, and the last one produces evidence.
Everything, not just code
Source is the smallest part of what a system knows. We read the record around it and build an organizational knowledge base.
- +Source code across C/C++, Python and COBOL
- +PRDs, commits, tickets, chats, logs, decisions
- =The context the retired engineer took with them
What the system must do
Before a line is rewritten, the behavior is written down — and the ambiguities get settled by a human, not guessed by a model.
- +Comprehensive PRD — the behavioral spec
- +Wiki for human support
- +Migration documents and verification plan
An AI loop with a harness, not a chat window
The agent works against the spec and the compiler, and every candidate is run against the original before it counts. Generate, verify, repeat until the behavior matches.
- +Differential tests: legacy output vs Rust output
- +PRD-derived acceptance tests, not tests inferred from the code
- +Unsafe quarantined and justified, or absent
- +Guidelines carried forward, so unit costs fall with every job
Everyone translates. Almost nobody proves.
The market has plenty of tools that emit Rust. The scarce good is evidence that the Rust does what the original did.
Uncontested quadrant. Code Metal's $1.25B valuation prices the category — and it sits on the defense side, top-down.
Free for one engineer. Seven figures for the enterprise.
One metric: validated KLOC. You pay for proven output — failed translations cost you nothing.
Finds what the retired engineer knew. The “it found the bug” moment, on your own repo.
Run a scanFull pipeline: spec, rewrite, and the proof — plus usage as you scale.
Talk to usOn-prem, audit trail, private models. Their code never leaves the building.
Talk to usCommitted program with verified-equivalence SLAs against a fixed scope.
Scope a programLand in 60 days. A free scan finds what the retired engineer knew. In the POC, your engineers watch the harness verify the rewrite — line by line.
Beta is live. Migrating real C projects today.
We publish results on MIT Lincoln Lab's open TRACTOR benchmark, refreshed every six months. No commercial vendor has done it yet.
Design-partner repos in active testing
The scan → spec → rewrite loop is running end to end on real codebases.
Three open-source C → Rust migrations
ubase, Cano and MicroPython testbeds — every benchmark published and re-runnable.
The optimization loop is running
Token use, memory and recursive self-improvement — unit costs fall with every job.
“The biggest risk in AI coding is not that the model cannot write code. It is that nobody can prove the model understood the system.”
Whoever proves equivalence owns the market.
A 45-minute deep dive on a live C repo: scan → spec → rewrite → verify. We show the proof, not the pitch.
lawrence@codeatlas.tools