Velona AI

Velona AI

AI that finds hidden fleet costs before they hit your P&L

125 followers

Autonomous AI agents that find hidden fleet costs, predict failures, and identify risks in real-time. Works with any telematics provider or OEM. Built on Databricks. Your data stays yours. Get answers in seconds, not weeks.
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What do you think? …

Ray Hernandez
Hey Product Hunt! 👋 I'm Ray, and I've spent almost 10 years in the fleet industry watching operators struggle with the same impossible challenge: How do you find actionable insights when you're drowning in millions of data points? THE PROBLEM Fleet operators generate thousands of data points per vehicle per day. But when they need critical answers—"Which vehicle will fail next?" "Where am I bleeding money?" "Which driver represents elevated risk?"—they face the same frustration: - Pull reports manually and hope to spot patterns - Submit requests to data teams and wait days or weeks - By the time analysis arrives, the vehicle has already failed Even companies with full data science teams couldn't escape this cycle. THE SOLUTION Velona uses autonomous AI agents that actively search fleet data in real-time instead of waiting for someone to ask the right question. Built on Databricks' Data Intelligence Platform, Velona finds the needles in the haystack—hidden cost centers, rising risks, and impending failures—then surfaces precise, actionable recommendations. KEY FEATURES 🔍 Autonomous Intelligence - AI agents proactively hunt for patterns humans and dashboards miss 🔧 Hardware Agnostic - Works with any telematics provider, OEM feed, or data source ⚡ Real-Time Analysis - Get answers in seconds, not weeks 🎯 Specific Recommendations - Not vague insights—actual answers with recommended actions 🔒 Privacy-First - Your data stays yours, never sold or remarketed 🏢 Enterprise Scale - Battle-tested with global fleet datasets BUILT FOR Fleet operators of 15+ vehicles across any industry—trucking, delivery, service, municipal, construction, and more. WHY WE BUILT THIS After years in the industry, our team at Vinli saw the same problem everywhere: the data to make better decisions already exists, but finding it takes too long. Velona solves that. TECH STACK - Databricks Data Intelligence Platform - Delta Lake for optimized storage - MLflow for model development - Unity Catalog for governance - Agentic AI architecture Limited early access launching today. Really excited to share this with the PH community! Happy to answer any questions about how it works, the tech stack, or what we learned building agentic AI for fleet management. What questions would you want your fleet data to answer automatically?
Chilarai M

@raydawg88 great launch. Congrats

Ray Hernandez

@chilarai Thank you so much! We're super proud!

Alex Cloudstar

Congrats Ray and team. Love the proactive agents that flag hidden costs, risks, and likely failures before they hit the P&L. Hardware agnostic and privacy-first is a win. How do you minimize false positives and alert fatigue for ops teams? Excited to see more.

Ray Hernandez

@alexcloudstar Thanks Alex! Great question. Alerts & Automation is exactly why we built the system the way we did.

We tackle false positives through a two-layer architecture. Our analyst agents (fuel, maintenance, safety, etc.) don't just flag anomalies. They calculate actual dollar impact and filter out anything below a meaningful threshold. So instead of "battery voltage dropped," you get "Unit 2847's battery will fail in 12-18 days, $380 tow + $1,200 downtime." On top of that we have years of data to compare against to make sure an anomaly looks like something we've seen in the past (or close to)

But the real breakthrough is our persona agents. They interpret the technical findings through the lens of someone who's actually run fleets. Understanding things like "this driver's harsh braking looks bad in the data, but he's hauling through the Rockies where it's normal" or "this maintenance delay is actually smart because the part is backordered anyway."

Every insight comes with specific vehicle IDs, dollar amounts, and the political context a real fleet manager would consider. We'd rather surface 5 high-confidence issues worth $10K than 50 maybes worth $100.

The team's been amazing on this. Especially the folks who've lived in fleet operations and know what actually keeps managers up at night versus what's just noise in the data.

Vitalii Romanchenko

great product!

Ray Hernandez

@mike4747 Thank you!

AI Vortex

This is super well executed 👏

Been deep in this space myself — got some interesting results with early users saving hours every week.

Shared a bit of that journey in my profile if you’re curious.

Henry

Congrats on launching Velona AI, Ray! The idea of autonomous agents proactively finding issues before being asked is really smart – solves the 'don't know what to ask' problem for fleet managers. Curious how these agents learn or are trained on what constitutes a significant cost or risk versus just normal operational noise?

Lilou Lane

Finally, something built by people who get fleet data pain. 😅 Can confirm: by the time a report is ready, the truck’s already down. Excited to see how Velona handles predictive maintenance in mixed OEM environments.