Business analysts waited hours-to-days for SAP developers to pull reports from a database of 100+ tables with cryptic column names (VBELN, KUNNR, AUART). The goal: a conversational bot that answers with a visualisation in under 30 seconds. Each schema — DDL plus five sample rows — is indexed offline into Qdrant with BGE-M3.

At query time a 5-stage pipeline runs: the LLM expands the question into schema-aware searches, a self-refinement step validates and augments the retrieved table set, a dedicated call generates SQL against the refined DDLs, and execution feeds Pandas preprocessing plus two auto-generated chart specs.

• Query expansion. SAP names never appear in plain English — the LLM expands each question into 3-5 schema-aware searches.
• Split the work. Table selection and SQL generation in one prompt degraded accuracy; two focused calls fixed it.
• Tame big results. Pandas summaries + top-50 rows beat dumping everything into context; exactly two charts keep the JSON parseable.