Regulated industry · Agentic platform

An AI workforce for compliance-critical operations

We built the data architecture and agent runtime for a platform where a wrong answer is a regulatory event. Agents investigate, draft audit-ready reports, and prepare decisions; a human approves every one.

Data architectureAgent runtimeAuditabilityRegulated AI
ResearchDraftActClassifyLearnhuman approves
The challenge

The work was governed by rules where being wrong is not a bug, it is a violation. Knowledge lived in documents, records, and the heads of a few experts. The team needed automation that was demonstrably correct, fully auditable, and that never acted on its own.

What we built
  • An ontology-based data model that unified records, observations, and procedures into one connected graph, the foundation everything else reasons over.
  • An agent runtime where each agent investigates, drafts, and prepares work, then hands it to a person. There is no path for an agent to act without approval.
  • An audit trail on every action: who, what, when, and the evidence behind it, so any decision can be reconstructed after the fact.
The outcome
  • Live across hundreds of facilities, in multiple regulatory regimes.
  • Every agent output is grounded in source and signed off by a human.
  • Work that took days of expert time is prepared in minutes, then reviewed.
FAQ

Common questions

We design the runtime so agents can only investigate, draft, and prepare work. There is no code path that lets an agent commit an action. A person reviews and approves every output, and that approval is recorded as part of the audit trail.

Every output is grounded in source records and carries an audit trail: who, what, when, and the evidence behind it. Any decision can be reconstructed after the fact, so a reviewer or auditor can trace a result back to the data that produced it.

The real risk is a confident wrong answer reaching a decision. We mitigate it with grounding, mandatory human approval, and a full audit trail. The system is built to make its reasoning checkable, not to be trusted blindly because it sounds right.

The data model is an ontology that unifies records, observations, and procedures into one connected graph. Rules and regimes are expressed as data over that graph, so adding a facility or a jurisdiction is a configuration change, not a rebuild.

Have a problem shaped like this?

If this looks like the kind of system you need, let's talk through it. First call is always free.

Start a project→