LLM Behavior Drift Sentinel

Continuously monitor output quality of the same model API and alert when it degrades

Problem

Many developers rely on closed-source model APIs but can't sense behavior changes until users complain or they notice themselves. There's a flood of user reports claiming the same model version got noticeably worse within weeks, yet there's no objective continuous monitoring — only subjective feelings and scattered reproductions.

Solution

Users define a set of key prompts with expected behavior (format compliance, refusal boundaries, reasoning accuracy). The product automatically re-runs this benchmark batch daily and after each model release, comparing historical outputs and producing a drift score with diff highlights. Alerts fire on anomalies, and trend charts show changes over time.

Users

Indie developers and small AI product teams relying on third-party LLM APIs, AI application operators

MVP

20–50 built-in benchmark prompt templates; daily scheduled calls to a specified model recording outputs and metrics; historical trend chart of drift scores; email/webhook alerts when quality metrics drop significantly

Revenue

Subscription based on number of monitored models and check frequency; free tier with one monitor and weekly checks, paid tier for multiple models and daily checks

Why now

Closed-source models update frequently without stability guarantees, making observability of output quality a fast-growing need; big cloud vendors won't build cross-model third-party monitoring