Why I built Kriv AI — and why governed is the only thing that matters.

I've spent time inside regulated industries — healthcare, insurance, finance — watching how AI actually lands. Not in demos. Not in pilots with hand-picked datasets. In production, where a wrong answer reaches a real patient, a real claim, a real trade.

What I kept seeing was a gap. Not a capability gap — the models are extraordinary. The gap was between what organizations deployed and what they could actually defend. AI that answered questions no one had scoped. Agents that took actions no one had approved. Systems that worked in the demo environment and drifted in production. No audit trail. No named owner. No way to explain what happened when something went wrong.

That's not a technology failure. It's a delivery failure. The consultancies brought in to solve it typically arrived with large teams, long timelines, and a methodology built for a different era. The teams doing the work were junior. The people who understood the problem weren't in the room. Six months later, the client had a process map and a set of recommendations — and still no governed AI in production.

What "governed" actually means here

Every system we ship has a named owner for every agent action. Every request is logged. Every deployment is aligned to the NIST AI Risk Management Framework before it goes live — not as a checkbox, as a design constraint. We build for the audit that will happen in two years, not the demo that's happening next week.

That discipline is not a premium add-on. It's the default. Because in a regulated industry, the cost of an ungoverned AI failure — a PHI leak, a biased underwriting decision, a hallucinated clinical summary — is not a product bug. It's a regulatory event. And those don't get fixed with a hotfix.

Why we're right-sized

Kriv AI is not trying to be McKinsey. We're not trying to replace your IT department. We're a small team of senior practitioners who scope, build, and deliver — personally. The person you talk to in the first call is the person who does the work. There's no handoff to a bench. No discovery theater that lasts a quarter.

That model isn't a constraint we're apologizing for. It's the reason we can deliver in weeks instead of quarters, at a cost that matches the actual scope of the work. We've shipped enterprise Claude Code training programs, governed agentic builds, and AI readiness assessments for organizations that needed results — not a roadmap for what results might look like eventually.

Who this is for

If you're a CIO or technology lead in healthcare, insurance, or a regulated mid-market business, and you're trying to figure out how to move on AI without creating a compliance problem — that's the conversation we're built for. We're not the right fit for organizations that want a long consulting engagement or aren't ready to act. We work best when there's a real problem, a real timeline, and someone in the room who can say yes.

I'm happy to talk directly. No sales process. A 30-minute call with a practitioner — not a business development rep — who will tell you honestly whether we're the right fit.

— Abhinav Dangri