Regressionwatch
A managed regression sentinel that maintains a minimized versioned evaluation corpus, records provider-change evidence, reruns pinned tests, and routes diffed quality candidates through accountable review and release decisions.
Mid-market AI teams can see output quality change when a model provider updates routing or releases a new version, yet general observability and continuous-integration tools may not trigger specifically from provider events. The research confirms three adjacent commercial products, two early evaluation components and no reviewed product dedicated to the provider-change-to-regression loop. That absence is bounded to the reviewed set.
Regressionwatch should separate suspected provider change and evidence, endpoint and resolved model identity, evaluation-corpus source and rights, corpus and assertion versions, run configuration, raw response, deterministic checks, rubric-based judge output, uncertainty, human reviewer finding, regression classification, affected workflow, owner decision, mitigation, release, rollback, destination readback and production outcome. A model judge is not ground truth, and a score change is not provider causation.
The product must minimize or synthesize production examples, exclude secrets and unnecessary personal data, preserve permissions and retention, avoid sending restricted data to an incompatible provider, disclose sampling and statistical limits, and never claim universal hallucination detection, quality guarantees or automatic safe rollback.
Mid-market AI product, platform and reliability teams that depend on external model providers and need managed evidence when provider behavior changes.
AI product and reliability teams at mid-market companies are identifiable buyers.
A managed trigger and evaluation layer can scale across customers if corpus operations remain controlled.
The record does not prove which provider or evaluation barrier recently changed.
A defined mid-market reliability buyer, confirmed adjacent tools and no reviewed provider-event-triggered sentinel make the loop concrete.
Provider-event detection, model identity, corpus rights, statistical validity, judge reliability, buyer budget and differentiation from existing evaluation products need validation.
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