Databricks Assistant
6 use cases using this technology
Maya is simplifying banking for diverse populations
Maya
Maya, the Philippines' leading fintech company, partnered with Databricks to modernize its data architecture into a unified lakehouse on Delta Lake, centralizing 100% of its data for real-time access and AI scalability. Maya's ML-powered underwriting tools cumulatively disbursed $2.1 billion in loans by year-end 2024. AI-driven fraud detection, combining transactional sequence embedding, network analysis and predictive modeling, achieved up to a 98% reduction in fraud losses. Hyperpersonalized, AI-driven marketing campaigns increased average revenue per user (ARPU) by 45%. Using Unity Catalog for governance and automated ETL pipelines, Maya also achieved a 28% increase in storage capacity without a rise in costs.
A Journey Towards Unified Data Governance at bp
bp
bp implemented Databricks Unity Catalog to unify data governance across data previously spread across disparate systems, migrating over 270 Databricks workspaces from two separate data hub platforms and 15 standalone instances, serving 10,000+ users across Finance ERP, Customer and Products, Production and Operations, and Enterprise business entities. The migration, run in four phased quarters, delivered centralized governance, granular row/column-level access control, and automated column-level data lineage and audit logs. bp expects the consolidation to save nearly $1 million annually plus roughly $4,000 weekly by eliminating manual Overwatch job management, and the migration unlocked Delta Sharing, SQL Serverless Warehouses, and further AI features like model serving and fine-tuning.
Hafnia modernizes maritime operations with Databricks Lakebase
Hafnia
Maritime tanker operator Hafnia built a 'data supermarket' on the Databricks Data + AI Platform and adopted Lakebase, a managed Postgres database natively integrated into Databricks, to run operational applications alongside analytics. This powers Hafnia's internal DNA Port operating portal and the Marvis AI copilot. Time to deliver production apps dropped from about two months to four or five days (90%+ reduction), and a 10-person data team now supports 10 departments, 200+ vessels and 4,500+ crew.
TK Elevator: Moving Beyond with Data + AI Driven Service Intelligence
TK Elevator
TK Elevator (TKE), one of the world's largest providers of elevators, escalators and vertical transportation systems, adopted the Databricks Platform to unify and process more than 500 million IoT telemetry events daily from connected elevators across 30+ countries, moving from reactive to predictive maintenance. TKE's MAX platform generates predictive insights that feed actionable guidance to field technicians via its Digital Operations Center (DOC), which generates 30,000-40,000 tickets annually and has reduced technician troubleshooting time on call-backs and unplanned shutdowns by up to 20%. The platform scaled from 3 to over 40 markets (10x expansion), cut data pipeline delivery from months to weeks, and reduced data sharing time with internal/external consumers from three months to three days (90% reduction). TKE uses Delta Lake, Delta Sharing, Lakeflow, Unity Catalog, Databricks Assistant, MLflow, AI Gateway, Vector Search and Model Serving, and is exploring agentic AI to combine technical, service and commercial context in real time.
HP uses Databricks AI/BI Genie to enable self-serve analytics across the enterprise
HP
HP's Analytics and Insights, product management and Digital GTM teams adopted Databricks AI/BI Genie to let business users ask natural-language questions of billions of telemetry events instead of waiting on engineers to build dashboards or write SQL, improving efficiency in analyzing product, sales and customer patterns by 40-50% and empowering thousands of users with self-service analytics, governed by Unity Catalog role-based access control.
Lippert Improves Customer Support with GenAI
Lippert
Lippert, a $3.8 billion global manufacturer of components for RV, marine and automotive brands, used Databricks and Agent Bricks to consolidate fragmented data into a single lakehouse and deploy AI agents for customer support. An AI assistant trained on product manuals, technical case history and field-expert videos surfaces troubleshooting guidance for call center agents in real time, cutting new-agent ramp time from six months to four weeks (an 85% reduction). Using Agent Bricks' evaluation framework and synthetic data, model accuracy for the support agent improved from 33% to 84% within weeks. AI also analyzes thousands of support calls daily for coaching and quality scoring (versus a prior manual sample of 100 calls/month) and automates call summarization into Salesforce. The initiative is expected to save millions per year and reclaim hundreds of thousands of hours, with new agents planned for HR, warranty and supply chain.