See drift early.
Fix it before users notice.
Production quality + drift monitoring with live dashboards, SLA alerts, root-cause triage and retraining feedback loops — so you ship the model and we keep it sharp.
Built for production, not just demos.
- Production sampling + scoring with calibrated reviewers
- Drift detection across topics, languages, demographics, and intents
- Pre-defined SLA bands with auto-alerting
- Root-cause triage on drift signals
- Catalog + retrieval quality monitoring
- Retraining + RLHF feedback loop
- Live dashboards + monthly executive reports
- Multi-tenant + multi-model support
How a typical engagement runs.
Define
Set SLA bands, sampling rules, alert thresholds, and the failure-mode taxonomy.
Sample
Production traffic sampled (stratified by slice) and routed to reviewer pool.
Score
Multi-rubric scoring + free-form rationale; drift signals computed per slice.
Alert
Auto-alert into Slack / PagerDuty / Linear when SLA breached or drift detected.
Iterate
Triaged samples enriched as training data; closed-loop into your retraining.
What you get in your bucket.
Questions, answered.
What sampling rate makes sense?
We typically start at 1% and adjust based on observed variance. For high-stakes domains (medical, legal) we recommend 3-5%.
How do you detect drift?
Topic-distribution shift, demographic-slice accuracy delta, query-intent distribution, and rubric-score trend with statistical significance.
Does this replace my model monitoring stack?
Complements it. Your stack monitors infra and basic metrics; we monitor accuracy + safety + bias on a sampled human-graded basis.
Multi-tenant?
Yes — segment by tenant, region, version, or any custom slice. Per-tenant dashboards and alerts.
Ready to build
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