Product descriptions:
Vinli, a leader in fleet data orchestration and mobility intelligence, has launched Velona™, a next-generation AI-powered fleet management platform built on the Databricks Data Intelligence Platform. Designed to help fleet operators find unseen cost savings, proactively surface operational risk, and automate manual workflows, Velona leverages agentic AI to connect diverse vehicle and driver data with actionable business outcomes. Through deep integrations with OEMs, telematics providers, and driver mobile devices, Velona ingests and unifies global fleet datasets while maintaining strict privacy standards. Databricks empowers Velona to operate at enterprise-grade scale and governance, delivering proven performance, accelerated development cycles, and robust security to fleets of all sizes.
Bringing Fleet Data to Life
Vinli’s mission is to turn disconnected vehicle and driver data into real operational and financial insight — a challenge magnified by the diversity of hardware, data protocols, and vendor systems in today’s fleets. With the rise of autonomous systems and connected vehicles, operators must manage increasingly complex streams of telematics and OEM data, which are often siloed across various platforms.
“Fleet teams need more than dashboards — they need a system that ties operational signals to financial outcomes and automates manual work,” shares Matt Himelfarb, CEO. Vinli recognized that finding hidden costs, predicting risk, and maximizing vehicle uptime would require a platform that orchestrates, normalizes, and analyzes data in real-time — without forcing expensive, rip-and-replace migrations.
Velona’s orchestration layer addresses these challenges by seamlessly integrating with existing telematics, OBD devices, OEM feeds, and driver mobile apps, providing unified visibility across every fleet touchpoint. Vinli’s strict privacy stance ensures customer data remains securely governed and never remarketed or sold.
Harnessing Databricks for Scalable AI
To achieve its vision, Vinli selected Databricks as the foundation for Velona’s data and AI stack. The Databricks Lakehouse architecture and Data Intelligence Platform enable Velona to seamlessly unify raw telematics, enterprise, and operational data for advanced analytics and machine learning workflows. Technologies such as Delta Lake, MLflow, Unity Catalog, and Lakeflow deliver robust data governance, optimized model operations, and efficient pipeline orchestration.
By leveraging Databricks throughout their software development workflows, Vinli realized a 40% faster time to market for Velona compared to legacy environments, accelerating enterprise innovation and operational efficiency. Early production metrics show a 2–3x increase in scalability and workload performance, enabling Velona to support higher concurrency and larger data volumes with minimal infrastructure overhead.
Databricks training was instrumental in achieving these gains. “Databricks training helped our engineers and data scientists standardize best practices for scalable data engineering and ML development,” Matt explains. Project onboarding time decreased by nearly 30%, and cloud compute costs dropped as pipelines and clusters were optimized. Model development moved from weeks to days, deployment velocity increased by over 40%, and redundant ETL workloads decreased by 25%, allowing Vinli to focus on real-time inference and simulation.
Driving Measurable Impact and Continuous Innovation
Velona’s deployment is already delivering tangible business value for Vinli and its customers. Faster feature delivery, improved system reliability, and tighter cost control are measurable outcomes stemming directly from Databricks-powered innovation. The ability to automate risk prediction and surface actionable recommendations is driving reductions in downtime, liability, and operational expenses for fleet operators.
The platform’s advanced analytics empower managers and dispatchers to make rapid, informed decisions, increasing productivity and responsiveness fleet-wide. Databricks' cost efficiency and near-real-time AI model serving ensure Velona remains competitive as customer fleets scale globally.
“Databricks has empowered us to bring Velona to market 40% faster, enabling real-time intelligence and decision automation across massive numbers of connected vehicles worldwide,” Matt summarizes. As Vinli expands Velona’s agentic AI capabilities, ongoing Databricks training in multi-agent orchestration and real-time observability will be crucial to unlocking future gains. Together, Vinli and Databricks are redefining connected-fleet management for the era of pervasive data and intelligent automation.