A trucking company with 2,000 vehicles experienced 15 roadside breakdowns per week, each costing an average of $8,000 in towing, emergency repairs, delayed deliveries, and driver downtime. Calendar-based maintenance schedules either serviced vehicles too early or too late.

OBD-II diagnostic data from vehicles was collected but never analyzed for predictive patterns. Maintenance shops were overloaded during scheduled service weeks and idle between them. Parts inventory was managed reactively, with 25% of repairs delayed waiting for parts.

The company needed predictive maintenance that could prevent breakdowns while optimizing shop utilization.

The Solution

Agents that stream diagnostics, score health, predict failures, and schedule maintenance

Vijan.AI deployed 4 agents. The Telemetry Ingester streams OBD-II data from all 2,000 vehicles including engine codes, oil condition, brake wear, tire pressure, and battery health. The Health Scorer rates each vehicle on a composite health index updated daily. The Failure Predictor estimates breakdown probability per component for the next 30 days. The Work Order agent schedules preventive maintenance in CMMS, balancing urgency against shop capacity and parts availability.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Maintenance

Fleet Maintenance Agent

Centralized Fleet Health Orchestration

Aggregates diagnostic data from all vehicles and autonomously coordinates maintenance, safety, driver, and fuel optimization activities to maximize fleet uptime and ROI.

Input

Telematics streams, service schedules, safety reports, driver scorecards, fuel data

Output

Coordinated action plans, priority alerts, resource allocation recommendations

Calls telematics platform for real-time vehicle health across entire fleet
Calls service database to cross-reference maintenance history and upcoming intervals
Autonomous decision: prioritize critical maintenance, safety audits, or training needs
Routes tasks to Safety, Performance, and Fuel agents based on urgency

Step 2 · Safety

Safety Compliance Agent

Regulatory Compliance & Inspection Management

Tracks DOT hours-of-service, vehicle inspection deadlines, and driver certifications, autonomously scheduling compliance activities and flagging violations before they result in fines.

Input

Driver logs, inspection records, certification expiry dates, DOT regulations

Output

Compliance alerts, inspection schedules, violation reports, certification renewals

Calls DOT API for hours-of-service validation and regulatory updates
Calls inspection database to identify vehicles due for annual or 90-day checks
Autonomous decision: ground vehicles or drivers nearing violation thresholds
Routes compliance gaps back to hub for executive visibility and remediation

Step 3 · Performance

Driver Performance Analyst

Driver Behavior & Training Optimization

Analyzes telematics for harsh braking, speeding, and idling events, autonomously recommending coaching sessions or incentive programs to improve safety scores and reduce wear-and-tear.

Input

Telematics events, safety incidents, fuel consumption, delivery performance

Output

Driver scorecards, training recommendations, recognition awards, risk profiles

Calls telematics scorecard API for acceleration, braking, and speed metrics
Calls training database to match behaviors with appropriate coaching modules
Autonomous decision: assign training, issue warnings, or reward top performers
Routes performance insights back to hub and to fuel optimizer for correlation

Step 4 · Fuel

Fuel Cost Optimizer

Fuel Efficiency & Cost Reduction

Monitors fuel card transactions and telematics to detect idling waste, suboptimal routing, and price disparities, autonomously suggesting fuel stops and route adjustments to minimize costs.

Input

Fuel card data, telematics idling time, route plans, fuel price feeds

Output

Fuel spend reports, optimal refuel locations, idling reduction targets

Calls fuel card API for transaction history and price benchmarks by region
Calls route optimization engine to identify fuel-efficient path alternatives
Autonomous decision: recommend fuel stops at lowest-cost stations on route
Routes fuel savings opportunities back to hub and driver performance agent

Results

Measurable impact within 90 days of deployment

40%

Fewer Breakdowns

Roadside breakdowns reduced from 15 to 9 per week. Average breakdown cost reduced 35% with pre-staged parts.

$4.8M

Annual Savings

Combined savings from fewer breakdowns, optimized maintenance schedules, and reduced parts inventory.

88%

Prediction Accuracy

Failure predictions accurate to within 7 days for 88% of major component failures.

30%

Better Shop Utilization

Maintenance shop utilization smoothed from feast-or-famine cycles to consistent 85% utilization.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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