Key results
The company
Vinli
vin.liConnected car data platform for fleet management and mobility intelligence.
Result highlights
- 25% reduction in redundant ETL workloads
The story
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 systems. Teams relied on manual workflows and static dashboards that failed to identify hidden costs or predict vehicle failures proactively.
The company developed a platform on Databricks that ingests and normalizes data from OBD devices, OEM streams, and mobile apps without requiring hardware replacement. Agentic AI models analyze this unified record to automatically surface recommendations for reducing fuel spend and flagging high-risk drivers. Unity Catalog ensures strict data governance across the environment, while Delta Lake handles the scale required for real-time inference and simulation.
Scope & timeline
- 40% faster time to market
- Nearly 30% reduction in project onboarding time
- Model development time cut from weeks to days
- Over 40% increase in deployment velocity
Quotes
“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.”