The evaluation layer designed for the agentic era
Purpose-built for agents, tool calling, and RAG. Not another LLM-as-judge wrapper - actual deterministic evaluation models trained specifically for the complexities of production AI.
The evaluation API built for agents, tool calling, and RAG. LLM as a judge doesn't score reliably, Composo does.
Everyone's trying to build agents. But they keep breaking in production - tool calls fail, outputs hallucinate, and LLM-as-judge can't catch any of it.
Purpose-built for agents, tool calling, and RAG. Not another LLM-as-judge wrapper - actual deterministic evaluation models trained specifically for the complexities of production AI.
Debug agent failures and hallucinations in minutes. Know exactly which tool calls break and which outputs hallucinate.
Deploy agents knowing they won't randomly fail or hallucinate. Catch errors before customers do.
Bridge the gap between 'cool demo' and 'production-ready.' Fix tool calling errors and accuracy issues that matter.
Drop-in replacement for LLM-as-judge. Works instantly for agents and tool calling where others fail.
Built for enterprise-scale teams, with robust compliance and secure integration into your stack.
92% accuracy vs 72% for LLM judges. Catches hallucinations and tool calling errors others miss.
The difference between 90% accuracy (fine for chat) and 99.9% (required for agents) is everything.
First to solve deterministic evaluation for tool calling. Zero hallucinations tolerance.
Single-sentence custom criteria for any domain. Medical, legal, financial - we handle complexity.
Proven results across startups & enterprises in the most complex verticals
CEO
Ex-McKinsey, Quantum Black
Oxford University
Founding Engineer
Ex-Tesla & Alibaba Cloud
Imperial College London
Founding Engineer
Ex-Thought Machine, Durham & Imperial College London
CTO
Ex-Graphcore ML Engineer
Oxford University
If you're shipping agents & LLM features in the next month and can't afford hallucinations or failed tool calls, we should talk.