Ingest schema metadata from Postgres, Snowflake, and Excel exports into an open-source data catalog (OpenMetadata). Each table and column is tagged with owner.

Fine-tune AI model on 400 historic analyst queries paired with their validated SQL. Embed warehouse-specific functions and naming conventions right into the system prompt, so the model can translate “show late deliveries by carrier” into a parameterized query in under a second.

Chunk SOP docs, data-definition PDFs, and KPI glossaries into a vector index (Pinecone). Before every answer, the assistant retrieves the most relevant passages—e.g., “LateDelivery is defined as >30 min past ETA.” The LLM weaves these snippets into the reply, embedding citation links for traceability.

If the user’s intent is ambiguous—“Give me costs by region”—the bot auto-asks follow-ups: “Which cost buckets? Which fiscal period?” It stores resolved patterns as reusable templates (“Regional Cost Drill-Down”) available via slash-command.

Generated SQL runs first in a row-limited sandbox to catch performance outliers and privacy breaches. The query plan and sample rows are shown for manager approval; one-click execution then hits production. Audit logs flow into Splunk for compliance.

Embed the assistant in Slack, Microsoft Teams, and the BI portal. Weekly fine-tunes incorporate new schemas and manager feedback, while usage analytics flag low-confidence topics for human curation.