Evals before answers
A compliance answer without an eval suite behind it is a liability. We build the harness first.
Regulated industries.
Regulation is dense, changing, and unforgiving of confident wrong answers. We build AI that respects that: monitored, auditable, guardrailed, with the receipts.
The pattern we build
Across skilled nursing (a compliance build), aviation safety (an aviation-safety build), and regulatory-obligation monitoring, the same architecture recurs: authoritative sources ingested continuously, obligations and risk signals extracted with transparent logic, AI used for drafting and analysis under audit, and humans owning every consequential decision.
What that looks like in practice
Eval suites that test regulatory questions against known-correct answers before any release. Guardrails that force escalation rather than confident guessing. Score breakdowns an auditor can walk through line by line. And deterministic fallbacks, because a compliance workflow can't fail just because a model provider had a bad day.
How we work in regulated industries
In regulated work, "probably right" is a liability. The answer has to be auditable, and a human has to be able to sign their name under it.
Audit trails everywhere
Every AI-touched artifact is logged: inputs, model, output, reviewer. Provenance is the product.
Escalation by design
The system knows what it may answer, what it must caveat, and what it hands to a human. That boundary is engineered, not hoped for.
Evals are your evidence
The evaluation harness isn't a nicety here, it's the paper trail that lets a risk team defend a decision to a regulator.
Guardrails before features
We wire in the constraints, refusals, and escalation paths first, then build capability inside them, not the other way round.
Dense regulation, legible answers
We turn thousands of pages of obligation into answers a human expert can check, cite, and stand behind.
Why 48x here
Compliance leaders don't need another chatbot; they need a system whose every answer can be traced, tested, and defended. That is exactly the discipline, evals, guardrails, grounded retrieval, that we bring to every build.
Frequently asked
How do you handle hallucination risk?
Guardrails, retrieval grounded in your source-of-truth, and evals that fail the build when accuracy drops below the bar.
Can a human stay in the loop?
Yes. Escalation and human sign-off are designed in from the start, not bolted on at the end.
Is our data used to train models?
No. Your data stays yours. We build on it, not off it.
Facing a compliance-heavy AI decision?
We'll tell you honestly what should and shouldn't be automated.
Book a call- A reply within one business day, from the person who'd actually do the work.
- A candid read on whether we're the right team. No sales pitch.
- If we're not the fit, you still leave with a sharper plan.