I’ve been working on turning unstructured field data into calibrated metrics. Instead of normal RAG, I built a system where AI agents act as a metric engine. Architecture:
- Unstructured data goes into Postgres.
- Queue system (SELECT FOR UPDATE SKIP LOCKED) feeds it to Claude (Haiku/Sonnet).
- Claude outputs deterministic JSON metrics.
- Supabase RLS handles the multi-tenant isolation. It works incredibly well for scoring things objectively. Has anyone else built AI pipelines specifically for metric generation rather than chatbots? What edge cases should I watch out for?’ submitted by /u/bestekarx
Originally posted by u/bestekarx on r/ClaudeCode
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