Analytics that tells AI product teams where their AI fails user

Show HN: Analytics that tells AI product teams where their AI fails user

Analytics that tells AI product teams where their AI fails user is active in the show hn launch space.

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Traditional analytics tracks clicks. For AI products, you need to know: what was the user trying to do, did the AI help, and did they succeed?I built a demo of this. It ingests AI conversations and runs 3 workers (GPT-4o-mini): intent classifier, quality scorer (LLM-as-judge), and task completion detector. Results show up in a dashboard designed for PMs, not engineers.Stack: Python SDK (zero deps, async) → FastAPI → Supabase → GPT-4o-mini workers → Next.js dashboard.Demo with sample data (not live product, validating the concept): https://dashboard-xi-taupe-75.vercel.appThe sample data models an AI app builder. Interesting patterns: scaffolding works great (78% success), but API integrations

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Website: dashboard-xi-taupe-75.vercel.app