One runtime. Not ten tools bolted together.
The common way to deploy AI is to wire a model to a dozen services and hope the seams hold. Every seam is a place where nobody is in charge and nothing is written down.
Keystone collapses that into a single runtime. The discipline loads before the AI acts. The gate sits on every irreversible step as plain code. The ledger records each decision, signed and hash-chained, and the model is denied from writing it.
One place to run the employee. One place to check what it did.
Point the operating model at a greenfield build and it produced Keystone. Point it at legacy modernization and it produced Refactory. The reasoning behind both is the problem they answer.
It ran. Here is the record.
Not a demo. A governed AI employee stood up in a real cloud account, under real controls, serving real load.
The throughput and latency are broken out on performance. Every decision the employee made lands in the register, signed and hash-chained, and you can verify it yourself.
Four phases. The one that matters is Deploy.
Framing and building are table stakes. Deploy is where governance stops being a document and starts being enforced code in your account. Keystone is the Deploy phase of the engagement The Seat guides, and once it is live the employee runs in the office.
Frame
We write down what the employee is responsible for, what it may never do alone, and where the gate falls.
Build
Context is built as infrastructure. The runtime, the ledger, and the gate are assembled and tested against the frame.
Deploy
The employee goes live in your cloud under NIST 800-171 r3 controls. From here on, every decision it makes is on the record and every irreversible one waits for you.
Transfer
Your leader takes the seat. We hand over the runtime, the record, and the discipline, then step back. Or your people learn to run it from the start.