Siloed feeds trapped data in static dashboards. Agentic AI now unifies streams to predict failures and cut fuel costs.
A mobility intelligence company processes complex data streams from diverse vehicle hardware, telematics protocols, and vendor systems across global fleets.
Operators struggled to link operational signals to financial outcomes because data remained trapped in siloed OEM feeds and disparate telematics...
“Velona bridges the gap between operations and finance by automating data ingestion, normalizing disparate feeds, and using agentic AI to surface precise recommendations: where to cut fuel and maintenance spend, which drivers represent elevated risk, and which vehicles deserve attention before a failure. Leveraging Databricks gives Velona the scale, governance, and real-time analytics we need to deliver those insights reliably to fleets of every size.”
Connected car data platform for fleet management and mobility intelligence.
Specialists manually compared PDF data. AI now flags deviations and routes them via Teams, cutting processing from days to <4 hours.
Translating 25,000 ERP objects meant 1,250 hours of manual developer work. AI finished the job in 20 days with zero errors.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.