AI Quality, Evals, and Regression Monitoring
ML engineers, product managers, AI QA teams
AI quality can drift after prompt changes, model swaps, data updates, provider behavior changes, and new user patterns. Traditional uptime checks do not show whether the answer is still useful.
Continuous quality signals with trace context
AnoSys brings continuous evals, AutoJudge, model monitoring, policy checks, and production traces into one loop so quality issues are visible early enough to diagnose and route.
Run evals where behavior changes, then connect regressions to prompts, models, traces, releases, and users.
Score
Run quality, safety, relevance, policy, and custom evals in CI and production.
Monitor
Track output quality, latency, refusals, error rates, cost, and user impact after deploys.
Compare
Compare versions, prompts, models, segments, workflows, and release cohorts.
Gate
Route regressions to owners and use eval evidence to guide release decisions.
What supports it
Why teams buy it
- Reduce regressions from prompt, model, or workflow changes.
- Give product and ML teams evidence for quality and safety decisions.
- Protect customer trust by monitoring whether AI remains useful in production.
Can AnoSys run production evals?
Yes. AnoSys supports continuous production evals and AutoJudge-style scoring connected to traces and operational context.
Can product managers use the eval data?
Yes. Dashboards and reports can translate evals into product quality, customer experience, and business KPI context.