Own your AI — no lock-in
Bring or build any model, switch anytime; your data trains no one.
Bring or build any model, switch anytime; your data trains no one.
What this solves.
Betting your operations on one AI vendor means rebuilding — and renegotiating — every time the market moves.
Own your AI — no lock-in, governed.
Bring your own model — commercial or self-hosted — or build your own on your data, and switch providers whenever you want without rebuilding. Your corpus and your tuned data domain stay yours, and we never train on your data, so you keep both flexibility and negotiating leverage.
The routine work, picked up.
Not a demo of what an agent could do someday — the everyday work it takes on now, each job running under the same limits and landing on the same record as everything else.
Not a promise — something we can put in front of you.
Concrete behavior you can watch on your own work — each one shipped and governed the same way, not a claim for later.
The products behind this.
The same governed apps as everywhere else — here’s where this outcome comes from. Start with these; the rest of the platform is already connected.
The same three pillars hold under every outcome.
This outcome runs on the same governance as everything else on the platform — permissible access by construction, tamper-evident proof, and human-in-the-loop agent governance. Not bolted on afterward; the way the work happens.
Isn’t standardizing on your platform just trading one kind of lock-in for another?
No — because the thing most vendors lock you into, the model, is the swappable part here. Bring a commercial model, run one self-hosted, or build your own on your data, and switch whenever the market moves without rebuilding your apps, permissions, or record. Your corpus and your tuned data domain stay inside your boundary and stay yours, and we never train a model on them. What stays constant is the governance layer — permissible access, a tamper-evident record, human-in-the-loop control — and that’s the one piece you actually want constant, because it’s what keeps every model you run honest. The leverage stays on your side of the table.
Proof, not promise.
We never train on your data, so you keep both flexibility and negotiating leverage.