The judgment that runs your company lives in senior people's heads and fifteen-year-old code. I extract it into a governed, git-owned brain that your team and your AI agents can actually run on. Fixed price. Your infrastructure. Yours to keep.
How you price, what a good deal looks like, why the architecture is the way it is. It lives in three people's heads and a codebase nobody dares refactor. Every AI tool you buy starts from zero on the things that make you money.
Point an agent at your business without a substrate it can trust and it improvises. Confidently. The output looks right, reads well, and is wrong in the specific ways only your senior people would catch. So they re-check everything, and the leverage evaporates.
Teams now use AI to review AI. Gates that decide what ships, with no evidence they catch what matters. Ask what would happen if a checker silently degraded, and you get silence. This is the gap that widens as models get stronger, not narrower.
Structured sessions with the people who hold the judgment, plus a read of the code and documents where it hides. The goal is the reasoning, not just the facts.
Everything lands in plain, git-owned files: a navigable hierarchy with wiki-links and provenance on every claim, so retrieval is precise instead of probabilistic.
Written specs the agents read at runtime, freshness flags, human review gates, and a weekly curation ritual. Governance here is a discipline, not a promise of autonomy.
Your team learns to feed it, query it, and challenge it. Your agents get wired to it. Adoption is where brains live or die, so it is part of the scope, not an afterthought.
Microsoft, Google, and Glean are all building horizontal knowledge graphs over your data. They will be good. They will also live inside someone else's walls, priced per seat, shaped by someone else's roadmap, and impossible to take with you.
A brain you own is different: plain markdown in a git repository you control. Any model can read it today. Any model can read it in five years. If I disappear tomorrow, you lose nothing.
Currently advising a European industrial-automation group on exactly this build (under NDA). Their internal teams independently converged on this architecture after testing it against embeddings-based retrieval on their own data.
The audit rubric is not theory. It has been run end-to-end on four production systems at a mid-size B2B company, every verdict traced to a file, and it found the same two gaps in all four: no distillation, and no verified verifiers.
My own company runs on the same system: hundreds of decision records, specs, and knowledge files that AI agents read, act on, and update every day, with human gates on what matters. This page was drafted through it.
I built this for myself first. My own company runs on an AI-native operating model: a git-owned brain holding every decision, constraint, and piece of accumulated judgment, with agents that draft, check, and ship real work against it every day, governed by written specs and verified review gates.
Then a client asked for the same thing, and it turned out the method transfers: extract the judgment, structure it so retrieval is precise, govern it so it stays true. That is what I now do for companies that want to own their brain instead of renting one.
Engagements run through Xanadu Labs LLC. I take a small number of clients at a time, because extraction is senior work and I do it personally.
One week, $5,000, and you will know exactly where your AI build and your knowledge substrate actually stand, and what to do about it. If a Sprint makes sense after that, you will know precisely what it should build first.
Book a fit call or email directly: [email protected]