What it takes to make AI work inside a company is what it takes to train a model on how that company works. Read each row across: the thing on the left is already installed, and the thing on the right is what it becomes.
Company Brainprofiled in place
Grounds every answer in how the business actually runs, cited to the source.
The corpus a model of your own is fine-tuned against.
liveGoverned tool accessidentity · scope · audit
Safe, audited actions inside real systems, with no raw credentials in an agent's hands.
The real systems a model is trained and tested against.
liveSandboxes and deployscanned · reversible
Somewhere a non-engineer can ship an app or an agent without a ticket.
The environment where attempts run safely, over and over.
liveTraces and correctionscaptured in the work
What ran, what it touched, and what a person fixed afterwards.
Labelled data and human feedback, produced by your own people doing real work.
livePrivate evalsyour outcomes, not benchmarks
Whether a workflow actually worked, measured against the outcome you named.
The benchmark only you own, and the gate a candidate model has to pass.
in buildFine-tune and distilon your infrastructure
Not yet part of the transformation work.
Small models carrying the routine work, trained and served inside your tenancy.
roadmapwhat everyone else does
Authors a copy of the business and learns in the copy, even when the copy runs in your cloud. A replica cannot hold twelve years of exceptions in one company's order-to-cash.
what this does
Instruments the original: your real systems, during real work, judged by the people who own the work. Only possible because the transformation work was already being done.
Capture is the hard part to get, and it only happens inside real work. Nothing new gets installed.