Evals for production LLM agents
Teams shipping LLM agents can't answer a basic question: did this change make things better or worse?
Zoroval answers it with evals derived from your production failures, not generic metrics.
Working with a small number of teams as design partners.
How it works
Mine
We analyze your production traces and surface the failure modes your agent actually exhibits — grounded in your data, not a vendor's checklist.
Codify
Those failures become a per-team taxonomy with judge models calibrated against human labels, so the evals are ones you can actually trust.
Guard
Every change you ship is re-checked against your golden dataset — you know whether a push made things better or worse before your users do.
Who this is for
Teams running LLM agents in production (LangChain, LangGraph, or custom stacks) who are past the demo stage and now debugging failures they can't name, count, or catch before shipping.
Founder
Built by Chandan — ML/data engineering background (ex‑Truecaller, MTech in Data Science from BITS Pilani).
Zoroval grew out of years of debugging LLM systems by hand and wanting a rigorous way to do it.