FleetFlow: Real-Time Telematics & AI Route Optimization
Optimizing 2,400 Commercial Freight Vehicles with Sub-Second GPS Telemetry and Automated Dispatch
Production Architecture & Telemetry
Real-Time Telematics Map
GPS & Sensor StreamLive sensor ingest aggregating GPS coordinates, engine diagnostics, and cold-chain temperature sensors.
The Client Problem
A major freight carrier operating 2,400 commercial delivery trucks struggled with fragmented telematics feeds, escalating diesel fuel expenditures, and manual dispatch coordination. Dispatchers spent up to four hours every morning manually calculating delivery sequences across whiteboards and spreadsheets. Drivers encountered unexpected urban traffic jams, resulting in missed delivery windows and substantial driver overtime penalties.
Key Requirements Scoped
- •Sub-second ingestion of telemetry data from multiple hardware ELD and GPS tracker vendors
- •Live geospatial dispatch dashboard rendering 2,400 simultaneous vehicle markers with directional heading
- •Automated multi-stop route optimization algorithm considering vehicle cargo capacity, bridge heights, and delivery time windows
- •Cross-platform mobile driver application for turn-by-turn navigation and digital proof of delivery capture
- •Automated customer notification webhooks providing real-time tracking links with accurate ETAs
The Delivered Engineering Solution
Nexora Labs architected FleetFlow, a cloud-native logistics dispatch and telematics platform. We built a high-throughput event streaming backend using Apache Kafka and Go microservices on AWS, paired with a dynamic React and Mapbox web dispatch console. We developed a proprietary constraint-based route optimization engine and delivered an offline-first Flutter mobile application for commercial drivers.
Confirmed Implemented Features
Exact Technology Architecture
- • React
- • TypeScript
- • Mapbox GL JS
- • Tailwind CSS
- • Flutter
- • Dart
- • SQLite Local Caching
- • Go
- • Node.js
- • Apache Kafka
- • Redis
- • TimescaleDB (PostgreSQL)
- • Amazon S3
- • AWS EKS
- • Terraform
- • Docker
- • Datadog
Implementation Details & Architectural Notes
Telematics pings are ingested via high-concurrency Go microservices writing directly to Kafka message topics. TimescaleDB was selected to efficiently query time-series geolocation vectors. The web console utilizes vector tile caching to render thousands of dynamic vehicle paths without browser memory leaks. The Flutter driver app caches route geometry locally and synchronizes proof-of-delivery signatures in background queues.
Quality Assurance & Testing Execution
- Simulated load generation of 50,000 concurrent IoT sensor streams transmitting at 5-second intervals
- Geospatial polygon boundary testing verifying geofence entry/exit trigger accuracy
- Hardware battery drain optimization testing on commercial ruggedized Zebra Android tablets
Project Impact & Results
FleetFlow reduced total fleet diesel fuel consumption by 22% within the first four months of rollout. On-time customer delivery rates surged to 99.4%, and morning dispatch planning time was slashed from 4 hours to under 15 minutes.
Services Leveraged in This Engagement
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