How HP Built a GenAI Data Chatbot in 3 Weeks Using Databricks Mosaic AI to Recover 20-30% of Data Team Time

HP manages data ingestion and customer data products at a scale that few organizations match — with more than 200 million printers worldwide generating telemetry and customer signals across multiple business units. Analysts and data teams were spending significant time on manual SQL queries to answer business questions, creating a bottleneck that slowed decision-making and consumed engineering capacity.

HP standardized on Databricks Data Intelligence Platform running on AWS to centralize data operations. The team implemented Unity Catalog for data governance and discoverability, Databricks SQL for analytical queries, and Vector Search for semantic retrieval across internal datasets. The platform gave HP a foundation to run large language models natively within the data environment — without exporting data to external AI services.

The breakthrough moment came when HP's team built an internal GenAI data chatbot using a RAG (Retrieval-Augmented Generation) architecture powered by Databricks Mosaic AI and the DBRX large language model. The chatbot allows analysts and business stakeholders to query HP's internal data in natural language — replacing manual SQL for routine questions and freeing data engineers to focus on higher-value work. As William Ma, HP's Data Science Manager, noted: "This solution has quite a few pieces...The college intern implemented the end-to-end solution in less than three weeks."

The impact was measurable across cost and productivity dimensions. HP achieved 20-30% cost savings versus its previous AWS Redshift environment. The chatbot recovered 20-30% of data team time that had been spent on manual SQL queries. The platform now supports 600+ Databricks users across HP's global business units, with AI/BI Genie enabling self-service analytics beyond the traditional analyst team.