RakerOne the platform we had to build because nothing else did what regulated businesses actually needed from AI.
Every insurance carrier, hospital, law firm, and bank wants AI in production. The CEO wants it this quarter. But chances are… the compliance team killed every deployment last year.
RakerOne is the architecture that clears the review. Agents never touch systems of record. They produce intents: structured proposals for what should happen next. A deterministic layer sits between the agent and the system, handling schema validation, permissions, approval gates, and audit logging. Every exchange gets identified, authorized, and recorded.
The counterintuitive part: the harder the rules around AI, the more autonomy an operation gets from it. A deployment that couldn’t pass compliance review last year passes it now, because the unsafe action is mechanically impossible at the system boundary.
On top of that we ship playbooks. A playbook is an operating model for a real job—a broker submission, a claim triage, a clinical intake, a matter opening. It knows the shape of the work. It holds the field rules, the approval paths, and the quiet expectations teams have always known but never written down. Non-technical operators describe them in plain text and switch them on. First runs happen same-day.
Everything runs on one surface. People, agents, and systems of record looking at the same work in real time. Not a chat window bolted next to the real work, with everything else punted back to email and tickets.
This is the thing I’ve wanted to build for a while. Most projects in this list hit some version of the same wall—AI that could do the work, stuck behind governance that wasn’t built for it. RakerOne is the answer to that wall. The operation becomes the asset, not the demo.