Model Drift Monitor

Detect sudden quality drops in AI model outputs and get alerts

Problem

Developers rely on AI models (e.g., Opus) for multiple projects, but models can suddenly become 'dumb' and output quality drops, impacting work. No tools currently monitor model performance fluctuations.

Solution

A lightweight monitoring service: periodically call AI models with predefined test cases, compare output quality (via simple scoring or rules), and alert via email/Slack on anomalies.

Users

Independent developers and small teams heavily using AI models

MVP

Test case management; scheduled calls and result logging; quality scoring and baseline comparison; anomaly alerts

Revenue

Subscription based on number of monitored models and frequency

Why now

AI model reliability is a growing concern, developers need early warning tools, indie developers can build fast