Fleet maintenance, before the failure.
NeoFleetR is an AI-driven maintenance intelligence platform being developed to transform fragmented fleet data into explainable vehicle-risk signals and smarter maintenance priorities.
Currently in research and demo development. Capabilities shown are experimental.
Concept interface · sample data
Fleets already generate the data. The challenge is turning it into early, useful decisions.
Service histories, technician notes, mileage, inspections, fault codes and telematics often live in separate systems. NeoFleetR is being designed as an intelligence layer that connects those signals.
Bring data together
Maintenance history, usage, inspections, diagnostic records and available telematics.
Standardize the noise
AI-assisted extraction turns inconsistent maintenance records into usable fleet data.
Detect risk patterns
Models evaluate vehicle history and operating patterns for emerging maintenance risk.
Prioritize action
Surface the factors behind a risk signal so maintenance teams can decide what to inspect first.
Built around the vehicle, not the spreadsheet.
NeoFleetR is focused on practical fleet-maintenance decisions: what is changing, which vehicle deserves attention, and why.
Vehicle health profile
A structured view of maintenance history, mileage, usage and available diagnostic signals.
Predictive risk
Evaluate patterns associated with elevated component or vehicle maintenance risk.
Explainable AI
Show the evidence and contributing factors behind each maintenance priority.
Fleet insights
Reveal recurring issues, repair patterns, lifecycle behavior and operational trends.
Maintenance prioritization
Help teams focus limited maintenance resources on the vehicles that warrant attention first.
Data interoperability
Designed to work across heterogeneous fleet records rather than depend on a single data source.
Concept visualization · sample fleet data · not a live diagnostic system
See NeoFleetR at a glance.
A concept demonstration of how fleet risk could move from signal to an explainable maintenance decision.
Build the evidence before scaling the software.
NeoFleetR is following a demo-first, customer-informed development path. The MVP will be shaped by real fleet workflows and validated demand—not assumptions.
From fragmented records to measurable maintenance intelligence.
Demo first. Validate next. Build what fleets need.
A staged path keeps technical development tied to evidence and real operating needs.
Research + Demo
Data model, AI extraction, risk concepts and interactive product demonstration.
Customer Discovery
Interview fleet operators and test the highest-value maintenance workflows.
Customer-Informed MVP
Build the smallest functional product around validated needs and available data.
Pilot + Measurement
Compare predictions with real outcomes and quantify operational usefulness.
Franklin Robles
Automotive Engineer · Founder, NFR Solutions
Automotive engineering meets AI-driven fleet decision support.
NeoFleetR is a founder-led initiative by Franklin Robles, an automotive engineer developing a practical approach to fleet predictive maintenance. The work combines vehicle-maintenance logic, fleet data, artificial intelligence and evidence-based product development.
The immediate objective is not to claim a finished predictive system. It is to build a credible demo, validate the problem with U.S. fleet operators, obtain representative maintenance data, and use that evidence to define and test the MVP.
Help shape a better maintenance intelligence layer.
We are looking to learn from U.S. commercial fleet operators, maintenance professionals and data partners. If you manage fleet maintenance or relevant vehicle data, your input can directly influence the MVP.