Use Case
Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
1 min readupdated 2026-08-01
/ quick answer
A document-processing platform ran extraction for 300 business customers with no output monitoring beyond error rates. Continuous sampling and evals caught silent degradation after a model update.
Continuous sampling and evals caught silent degradation after a model update. A document-processing platform ran extraction for 300 business customers with no output monitoring beyond error rates. Outcome: After adding a 45-case eval suite and daily sampling, a provider-side model change showed up as a 19% accuracy drop within 36 hours. The team pinned the previous version, fixed the prompt and shipped with no customer-visible incident. This use case node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Situation
A document-processing platform ran extraction for 300 business customers with no output monitoring beyond error rates.
Tools Used
Workflow Applied
Outcome
After adding a 45-case eval suite and daily sampling, a provider-side model change showed up as a 19% accuracy drop within 36 hours. The team pinned the previous version, fixed the prompt and shipped with no customer-visible incident.
/ frequently asked
What is the Platform Catches a 19% Quality Drop Before Users Did use case?
A document-processing platform ran extraction for 300 business customers with no output monitoring beyond error rates.
What was the outcome?
After adding a 45-case eval suite and daily sampling, a provider-side model change showed up as a 19% accuracy drop within 36 hours. The team pinned the previous version, fixed the prompt and shipped with no customer-visible incident.
Which tools were used?
ai-observability-stack, ai-testing-stack.
↳ connected nodes
Workflow↳ linked
Build an Eval Suite Before Optimising Prompts
Stop guessing whether a change improved anything.
Workflow↳ linked
Monitor an AI System in Production
See quality, cost and failure drift before your users report it.
Tool Stack↳ linked
AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
Tool Stack↳ linked
AI Testing Stack
Test deterministic code and probabilistic AI output in one pipeline.
Dictionary↳ linked
AI Monitoring
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift.
Dictionary↳ linked
AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.