How do you audit your AI report metrics against database ground-truth?

Hey Product Hunt community! 🚀

As AI-driven reports and summaries become standard in enterprise software, we’ve noticed a massive bottleneck that dev teams face: accuracy auditing.

When an LLM summarizes a business report containing financial metrics, user stats, or KPI numbers, verifying that those numbers match your structured databases is a tedious, manual process.

We built Datamagics.ai to automate this using isolated query sandboxes that compare unstructured report claims against live SQL connections and CSVs simultaneously.

As we expand our roadmap, we’d love to hear from other builders:

1. How do you currently validate LLM extraction outputs against database records? (Are you writing custom unit tests, doing manual SQL sanity checks, or using another tool?)

2. What is your preferred vector database for production RAG? (We currently support Pinecone and Qdrant, but are planning to add pgvector, Milvus, and Weaviate).

3. What database connectors are most critical for your data workflows? (e.g., Snowflake, BigQuery, MongoDB).

Check out Datamagics.ai and let us know what features you’d like to see next!

Looking forward to your insights! 👇