it_support · manufacturing · workflow
HP builds a RAG knowledge chatbot and self-service analytics on Databricks Mosaic AI, reducing data team support burden and cutting platform costs by 20–30%
HP's data engineering teams were overwhelmed by partner and customer support requests, spending 20–30% of their time manually writing SQL queries and cross-referencing data—equivalent to a full additional headcount on a five-person team. Knowledge was scattered across internal wikis, SharePoint files, and team channels, hampering onboarding and real-time decision-making.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits support inquiry
Partners and users submit inquiries about restricted data access, platform features, or onboarding, constantly distracting the data engineering team.
Tools used
Databricks Data Intelligence PlatformDatabricks Mosaic AIDatabricks SQLUnity CatalogDBRXVector SearchAgent Bricks AI PlaygroundAI/BI Genie
Outcome
HP achieved 20–30% lower operational costs compared with their AWS Redshift warehouse and forecasts significant productivity gains as the AI agent chatbot reduces the need for manual partner support. An intern implemented the end-to-end RAG chatbot in less than three weeks.
Results
Time saved20–30%
Cost replaced20–30% lower costs
Grounding & classification
Source type: vendor customer story
34 fields verified against source quotes.
ai agentchatbotenterprise searchragknowledge basesupport ticketmetric backednamed customerproduction runtime claimedtools describedworkflow describedmanufacturingcost reductionemployee productivitytime savedvendor customer storyback office opshr onboardingit supportautonomous resolutionrag answering