Case Study: FactSet achieves scalable enterprise GenAI development with Databricks

FactSet Implementing an Enterprise GenAI Platform with Databricks

FactSet, a financial data and analytics provider, wanted to expand AI-driven features across its platform but was slowed by fragmented GenAI tools, inconsistent LLMOps, and governance challenges. To support products like FactSet Mercury and other chatbot and summarization experiences, the company evaluated a more standardized enterprise AI platform with Databricks.

FactSet implemented Databricks Mosaic AI, Databricks-managed MLflow, Unity Catalog, and related Databricks services to create a centralized LLMOps and model-serving framework. With Databricks, FactSet improved collaboration, lineage, and governance while accelerating development; results included over 70% lower latency for a Mercury code-generation use case and about 60% lower end-to-end latency for fine-tuned text-to-formula workflows.

FactSet

Kate Stepp

Chief Technology Officer