hyprel learns how your business actually works, then tests the AI agents that banks, insurers, capital markets firms and healthcare teams put in front of people, and blocks any release that gets worse.
Independent by design
Platforms love to report containment: 83% of sessions handled without a person. Nobody checks whether those answers were right, or what happened to the rest. It's part of why MIT found only 5% of custom enterprise AI tools reach production. hyprel reads every session independently, so your teams get evidence instead of a platform's word.
The new version gave three customers a wrong answer the old one got right. It doesn't ship until it's fixed.
What hyprel does between releases
Platform dashboards show containment and CSAT. They don't show what customers, patients or policyholders were trying to get done. hyprel groups every conversation by intent, so you see which jobs the agent handles, which it fumbles, and which it quietly drops.
In a critical industry, a fluent wrong answer is a compliance event, not a bad review. We check the whole agent, including tools, retrieval, memory and policy, and flag the wrong dispute deadline, the claim denied under a retired rule, or the account read to a caller who never passed verification.
The suite rebuilds itself as your business changes. New cases come in from real conversations and your latest documents, outdated ones are replaced, and it stays weighted the way your traffic is today. Real failures stay in as regression tests, so a failure caught once never ships twice.
Every model change, prompt edit and platform upgrade is replayed against the suite. If the new version is worse, the release gate blocks it, the same way change control holds a release of a core system.
How it works
We work with your team one-on-one and go deep: policies, SOPs, product and pricing documents, the rules you answer to, and real conversations and traces from your agent.
Tests drawn from what people actually ask, scored against what your documents and your experts say is correct, from Reg E to HIPAA to claims handling.
Each new version is replayed on the same conversations. Same or better ships. Worse gets blocked, with the traces your team or platform needs to fix it.
Why now
The models are good enough. What's missing is proof that an agent will behave with real customers, real data and a regulator watching.
That proof comes from evals. Garry Tan has said evals are becoming the real moat for AI companies, and they matter most where mistakes are expensive.
Missing proof is the true blocker to deploying AI inside regulated industries, and to the 10x gains in efficiency it could bring there, because one wrong answer is a compliance incident, not a bad review. That is why we work with one institution at a time, going deep into the business and helping measure what truly matters. Work with us, or come build it with us.
Why hyprel exists
“I built an enterprise sales agent used by thousands of people, and the eval harness a whole company shipped on. It taught me one thing: nobody actually knows if their agent works. That blind spot is everywhere, and it costs the most in the industries that can't afford to break: banking, capital markets, insurance, healthcare.”
Rahulraj Jhawar
Founder. Previously DevRev, BCG, BITS Pilani.
Walk us through one workflow and the documents behind it. We'll show you what your current testing misses.
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