{"schema_version":1,"snapshot_id":"bfb936f5ceae4a7427f93961201723779c3021545a3c4222395e8678314c3e55","generated_at":"2026-10-17T21:51:24.907033+00:00","notice":"Potential GTM matches from directory metadata and model triage. Discovered does not mean vendor-verified or tested. Pending records remain under review; pricing and install details require primary sources.","canonical_url":"https://gtmmcps.com/discoveries/a9c50cbeddbaa7e40e89","record":{"id":"a9c50cbeddbaa7e40e89","name":"ai·rete·rag","aliases":["ai·rete·rag"],"description":"ai·rete·rag splits deciding from explaining. The verdict comes from a Rete production-rule engine — deterministic, reproducible, and traceable to the exact rule that fired, with salience deciding which rule wins when two disagree. Only then does an LLM run, grounded by retrieval over your own policy documents, to render that verdict into prose that cites real policy text. The model never decides anything, so an explanation can't invent a threshold your rulebook doesn't have.\n\nEight tools cover the whole loop: hand it a written policy and get back draft rules that cite the sentence each one enc","repository_url":null,"sources":[{"source_id":"smithery","url":"https://api.smithery.ai/servers","observed_at":"2026-10-17T21:40:22.481400+00:00","source_record_id":"d804e66e-4193-41c0-a26b-f58f80efd0f7","source_record_name":"zaharajabeen13/ai-rete-rag"}],"relevance_signals":["commerce","e-commerce"],"classification":{"label":"pending","method":"not-yet-classified"},"catalog_ids":[],"verification_status":"discovered","vendor_ownership":"not-verified","execution_tested":false,"pricing_status":"not-verified","documentation_status":"not-verified"}}