Today most companies rent their AI: they send their data to a model they do not control and pay per answer. We map how your experts work, capture the data that only exists inside your company, write down what good looks like, and train a model you run where you choose and improve as your business learns.
For any company whose work depends on the judgment of its people and whose data cannot leave its walls.
An AI interviewer, by voice or chat, holds long structured conversations with the people who know the work, inside your company and, when you want, outside experts. It extracts how they decide, what evidence they use and where they fail, and keeps the conversation going as the work changes.
Agents run on your people's computers and inside your tools (chat, email, tickets, documents, code), with their consent, and record the work as it happens. Nothing leaves your perimeter. This is the data a rented model never sees.
Evals are the tests a model has to pass. Our forward deployed engineers write them with your experts: cases where the model has to answer like your best person, cases where it has to stop. Every version is measured against them before it reaches production.
One connected record of what your company knows and what is happening now, with a source for every fact. It feeds your model, your agents and your people.
The Company Brain →Trained on your evals and your data, on an open base model you choose, running in your cloud or your building. You keep the model, the weights, the data and the evals.
The senior engineers who build it work inside your team, under your lead, and stay until your own people run it.
Forward deployed engineers →The gap between the two is four jobs. We do all four.
We map each workflow with your experts and write it down as tests. This is the yardstick for everything that follows: nothing ships until it beats the rented model on your tests.
The interviews with your experts, the work captured inside your tools, and every answer an expert corrects become examples the model learns from. How your people decide, in their words, on your cases.
Every miss in production is logged, turned into a new test and a new example, and fed into the next version. The model gets better because your business runs, not because a vendor shipped an update.
When a better open model appears, your evals, your data and your adapters move with it. We retrain, measure against the same tests, and switch only when it wins.
There is no single technique. We pick by what your data and your evals say, and measure every step.
A small set of added weights trained on your examples, so the model answers the way your experts do without retraining the whole base. Cheap to train, cheap to update, and the adapter is a file you own.
Preference tuning: every time an expert fixes an answer, the pair becomes training data. The model learns what your people prefer, not what the internet prefers.
For high-volume work, a large rented model generates and grades examples, and a smaller model trained on them runs at a fraction of the cost, inside your walls.
What changes every week (prices, policies, customers) stays in the brain and is fetched at answer time, so the model does not need retraining for every change.
Your cloud, your private network, or a server in your building.
The owned model replaces the rented one only where it wins on your tests. Where it does not, you keep renting, and you know why.
Your first workflow mapped with your experts, your evals written, and the rented model you use today measured against them.
The Company Brain live on that workflow, capture agents recording the work, and the first owned model measured against the same evals.
Someone on your team operates the loop: new failures become tests and examples, and each new version is measured before it ships. One pod per workflow, month to month.
Some of the places it pays fastest.
Your customers ask where their data goes. An owned model is the answer, and a sales argument.
Underwriting and claims are expert judgment. It belongs in a model you own, not one you rent by the answer.
Your product is your partners' judgment. It should stay inside your walls.
Anywhere a credentialed expert is the bottleneck and the records cannot leave.
Names withheld. Each was built with the same pieces: the interviewer, the capture agents, the evals, the brain.
An AI interviewer runs two-hour structured interviews and is measured against a senior recruiter's own interviews on a 10-dimension rubric, with regression tests before every change. It screens engineering candidates today.
A software factory where 17 agents take a specification and return a verified pull request. It tests itself with 237 cases across 41 components, each control with a case that must block and a case that must pass.
A management brain of 18,000 connected facts, built every day from the emails and reports of a distributed team, that answers questions about people, incidents and decisions with the source attached.
One Company Brain across all three, fed automatically from email, meetings and work sessions, that gives every agent and every person the history of a decision before they act on it.
Chat conversations with prospective students coded by a model, each answer judged for quality and the agreement between passes measured, to find what the assistant gets wrong and fix it.
That is what we do.
A custom LLM for your company. Fine-tuned on your data, on an open base, with LoRA and other techniques, running in your cloud or on your servers. You own the weights.
LLM evals for your use case. A test set written with your experts that says, for every version of the model, whether it is good enough to ship.
Workflow mapping for AI. Your processes and the judgment behind them, extracted from your experts and written down so a model can learn them.
A private AI that never sends data out. Capture agents, brain and model inside your perimeter.
A Company Brain. One connected record of what your company knows, with a source for every fact, for your agents and your people.
Engineers on your team. Forward deployed engineers who build it with you and stay until your own people run it.
No. Most companies start with the brain and the evals on a rented model, and own the model when data, cost or control require it. The evals tell you when.
Inside your perimeter. Capture agents run on your machines, and the model runs in your cloud or your building.
Your evals, data and adapters carry forward. We retrain and measure before anything changes in production.
You own the model, the weights, the adapters, the data, the evals and the code.
No. It is for any company whose advantage is how its people work.
One working session: we map one workflow with you, write the first evals, and tell you whether owning the model is worth it in your case.