Challenge
A global Medical Affairs consulting partner needed answers from complex, multi-source datasets spanning CRM records, field interactions, and qualitative feedback. But even simple questions required deep knowledge of schemas, data relationships, and metric definitions, creating bottlenecks and heavy dependency on technical teams.
Traditional Text-to-SQL tools made things worse. Without understanding business semantics, they produced incorrect joins and misinterpreted metrics. In a regulated pharmaceutical environment, that kind of inaccuracy creates real risk.
Solution
Together with the organization, we built a Semantic Layer combined with a Knowledge Graph over their existing data platform. No migration needed.
The architecture includes a business ontology defining entities and metrics, a Knowledge Graph making relationships explicit and traversable, and a mapping layer linking business concepts to physical data. These power a GraphRAG pipeline that replaces probabilistic SQL generation with structured, deterministic reasoning, where every query is grounded in validated definitions before it runs.
The solution also unifies structured and unstructured data, so users can combine quantitative metrics with qualitative inputs like field notes in a single query.
Business Value
Business users now move from question to answer in seconds, in plain language, without technical intermediaries. Metric definitions are standardized across teams. Every answer is auditable and governed. And the semantic foundation grows with the organization’s data, not against it.
Download the full case study to see how we built it, step by step.
