Thirty years of decisions sit in your systems, and almost none of it can be recalled when it matters. The Enterprise Brain is the layer that turns that scattered record into one governed memory, and a small language model of your own, trained on your work and running inside your estate, that lets your people and your agents ask it questions in plain language.
Why this exists
Large organisations have spent two decades getting good at keeping things. They have not spent them getting good at finding things, and the gap shows up in the same three places in almost every enterprise we open up.
The answer to a single board question lives across the ERP, a shared drive, four years of minutes, a contract nobody has opened since signing and one person's inbox. No system holds all of it, so nobody can answer it quickly and honestly.
A number in a deck is only as good as the source behind it, and by the third slide nobody remembers where it came from. Anything a board acts on has to be able to show its working, which is exactly what most AI tools cannot do.
The person who knows why a decision was taken in 2019 retires, and the reasoning goes with them. Institutional knowledge is held in people rather than in the institution, and every exit is a quiet write-down.
The Enterprise Brain is not another place to put documents. It sits above the systems you already run, reads from them continuously, and becomes the single thing that everything else asks.
Connectors read from the ERP, the document estate, the contract repository, board packs and mail. Nothing is migrated and nothing is replaced, because a memory that requires a migration never gets built.
A semantic index makes text findable by meaning. An entity graph on top of it knows that a vendor, a contract, a payment and a board resolution are the same story, so the brain can follow a thread rather than return a list of files.
The brain answers each person only from what that person is already entitled to see, and every answer carries its sources. Nothing becomes visible because it was indexed.
The SLM capability
A small language model is one trained tightly on a narrow domain rather than on everything. For an enterprise that is not a compromise. All of its capacity goes to your work instead of to poetry and trivia, it runs on hardware you already own, and it is small enough to be frozen, versioned and audited like any other control. STAIR builds them.
We measure the task before choosing the model. A frontier model sets the standard on your own questions, then we distil that behaviour down into something small enough to own. What you end up with is not a weaker model; it is one that spends everything it has on your work.
Small enough for a single GPU in your own data centre, a modest instance in your cloud tenancy, or a box on the plant floor. Regulated material never crosses the boundary your auditors already understand, and no per-token meter runs against a vendor you do not control.
Trained on your contracts, your policies, your minutes and your house style, so it uses your vocabulary and your approval thresholds. Narrow beats broad here: a small model tuned on one enterprise's corpus often outperforms a far larger general model on that enterprise's own work.
A model you own is an artifact you can pin. Freeze a version for a year and the answer it gave in March can still be reproduced in December. That is the difference between a tool and a control, and it is what lets the second and third lines of defence sign off on it.
How it lands
A brain built for the whole enterprise on day one is a programme that never ships. We start with one function where the memory is already painful, prove it against that function's own standard, and widen from something that works.
Map where knowledge actually lives, connect the first sources, and stand the memory up against one function, most often finance, audit or legal. Permissions and provenance are built in at this point, not retrofitted later.
Run it against the questions that function is asked every month, side by side with how they are answered today. The evaluation harness is written here, so improvement becomes measurable rather than anecdotal.
Extend the memory across functions and put agents on top of it, each one operating inside the same permissions and the same audit trail. The brain becomes the thing every new AI initiative connects to rather than another silo beside them.
Where to start
Tell us the company and we will draft a blueprint for it, or speak to the founders directly about what the first ninety days would look like.
