Release-testing infrastructure for regulated AI agents. Domain-specific evaluation suites so teams know exactly what fails before they ship.
Companies in compliance, mortgage, legal, and healthcare are deploying AI agents at scale. But when the cost of a bad answer is legal, financial, or medical risk, you can't rely on LLM-as-judge or benchmarks scraped from the internet.
Asteria builds domain-specific evaluation infrastructure — trained on expert annotations, powered by reinforcement learning — so you know exactly what fails before you ship.
Expert-calibrated evaluation
Companies in compliance, mortgage, legal, and healthcare are deploying AI agents. But when the cost of a bad answer is legal, financial, or medical risk, you can't rely on LLM-as-judge or benchmarks scraped from the internet.
Today, evaluation is either nothing, unreliable, or unscalable. None of them tell you whether your agent is actually safe to ship.
No real evaluation at all. Teams smoke-test a few inputs, and if it looks right, they ship — discovering failures in production or in a customer's risk review.
LLM-as-judge or benchmarks scraped from the internet. They measure noise, not whether your agent meets a real domain standard where the cost of a bad answer is legal, financial, or medical risk.
Hire contractors to check every output by hand. Slow, expensive, and impossible to run on every version — exactly when you need it most.
Your domain experts define evaluation rubrics for your actual agent workflows. Compliance officers, underwriters, legal reviewers — the people who know what good looks like.
Expert annotations train a calibrated reward model specific to your domain. It learns to evaluate complex, multi-step agent traces against a real standard.
RL-powered adversarial testing discovers edge cases and failure modes your agents hit in production. Not random fuzzing — targeted, intelligent probing.
Before releasing a new agent version, know exactly what fails, why it fails, and whether it meets your deployment threshold. A release gate, not a rubber stamp.
AI agents reviewing documents against regulatory requirements. When the cost of error is legal risk.
Agents processing applications, verifying data, underwriting decisions. Where mistakes cost real money.
Agent workflows where accuracy is literally a matter of compliance, liability, or patient safety.
We're onboarding design partners building regulated AI agents. Join the waitlist for early access.