Composo offers an automated evaluation platform that connects to production LLM traces, identifies and categorizes domain‑specific failures, and lets experts correct them so the system improves detection of similar issues. The platform generates guardrail rules that block erroneous outputs in real time with sub‑second latency, and is deployed in 2–4 weeks with full handover of the taxonomy, guardrails, and annotation data to the customer.
Funding
$2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
4OTPFounders
Product
Problem
You lack visibility into the specific ways your production LLMs fail, and building and maintaining custom evaluation infrastructure is time‑consuming, brittle, and prone to drift as models and domains evolve.
Solution
Composo provides an automated evaluation platform that connects to your production traces, identifies failures, and categorizes them by type, severity, and frequency. Domain experts review and correct the flagged cases, and each correction automatically improves detection of similar issues. Confirmed failure patterns are compiled into guardrail rules that block erroneous outputs at runtime with sub‑second latency, running on inexpensive models rather than costly frontier LLMs. The system is deployed within 2–4 weeks, requires roughly 10 hours of expert effort, and hands over full ownership of the taxonomy, guardrails, and correction data to the customer. Ongoing maintenance and upgrades keep the platform aligned with evolving standards and model updates.
Target Audience
Primary customers are product and engineering teams that deploy LLM‑driven applications in regulated or high‑risk domains such as healthcare, legal, and customer support, and need reliable, automated quality control.
Features
- Production‑trace integration that surfaces domain‑specific failure modes beyond generic hallucination labels
- Dynamic failure taxonomy built from 30+ deployments, continuously expanded with anonymized cross‑customer data
- Expert‑in‑the‑loop correction workflow where each annotation compounds to improve automatic detection of similar cases
- Guardrail generation that enforces quality standards on every model output with sub‑second response time
- Self‑hosted deployment options on Azure, AWS, or GCP with SOC 2 Type II compliance
- Full handover of evaluation criteria, taxonomy, guardrail rules, and annotation data after the initial engagement
- Platform‑wide upgrades and tuning to adapt to model changes and new domain requirements