Most AI governance fails where it matters most: at runtime.

— Why I'm building this —

I'm Pavan Kumar. I've spent 14+ years in DevOps, DevSecOps, and secure software delivery — Principal Engineer at Palo Alto Networks, technical lead at Boeing, and a lot of cloud platforms in between.

In infrastructure, we already solved a version of this problem. You don't ship a deployment and hope. You enforce policy before it goes out — gates, guardrails, controls that act in real time.

Then AI showed up in the enterprise with none of that.

Today most AI governance works like this: deploy the model. Collect the logs. Review the outputs. Discover the fire from the wreckage. Write a report. Hope the next one goes better.

Governance by replay.

But AI doesn't act on paper. It acts at runtime — and control that lives only in reviews can't manage a system that changes every day.

Niti is the opposite. It sits in front of your AI traffic and enforces what's acceptable before the model responds — not after the audit. Pre-execution policy gates. Real-time monitoring. Guardrails that encode your rules and your regulatory obligations, including the EU AI Act.

And it runs on-prem. Your data never leaves your environment.

Governance by design, not governance by replay.

— Join the preview —

We're taking on a small group of design partners — teams running LLMs in production who want real control, not another report. You get hands-on deployment support, direct access to me, and design-partner pricing when we hit general availability.