Enterprise AI · Fleet Management · AWS IoT
AI-Powered Fleet Management System for Trianz
Trianz needed a fleet management solution capable of detecting unsafe driving in real time, streaming and analyzing video at scale, and handling the data demands of a Fortune 100 logistics client base. [x]cube LABS built it on AWS IoT with real-time ML at its core.
The Challenge
Legacy Fleet Systems Were Failing at Every Level
Trianz identified a critical gap in the fleet management capabilities available to their transportation and logistics clients. Existing systems could not detect unsafe driving behaviors as they happened. Video analytics were limited — live streaming, playback, and event-triggered analysis were either unavailable or inadequate. Managing and analyzing the volume of video data being generated across large fleets was becoming operationally unmanageable.
The result was high operational cost, reduced productivity, and a safety posture that depended on human attention at every stage — a fundamentally unscalable model. Trianz needed a purpose-built solution that could bring AI intelligence to fleet operations without adding operational complexity.
The goal was a fleet that could watch itself and alert humans only when a human actually needed to act.
The Solution
Lean Architecture Meets Real-Time Machine Learning on AWS IoT
The [x]cube LABS team designed and built a custom fleet management platform using Lean Architecture and Engineering principles, maximizing efficiency, scalability, and responsiveness while minimizing overhead. The system is built on AWS IoT services, enabling real-time ML inference directly at the edge and in the cloud.
The platform combines live video streaming with event-based triggers: the ML model monitors driver behavior continuously, surfacing alerts only when a defined safety threshold is crossed. Video is stored and indexed for playback and post-event analysis, giving fleet managers full historical context alongside real-time awareness.
Real-Time ML Safety Monitoring
Machine learning models analyze driver behavior continuously, detecting unsafe patterns — hard braking, lane departure, distraction — and triggering alerts in real time.
Live Video Streaming & Playback
Full live streaming capability with event-indexed playback, so fleet managers can review any flagged incident in context.
Event-Based Video Analysis
Automated video capture and analysis triggered by safety events, removing the need to review hours of footage manually.
AWS IoT Data Infrastructure
Scalable AWS IoT architecture managing the ingestion, storage, and processing of high-volume video and telemetry data across the entire fleet.
Lean Architecture Design
Every component built for minimal overhead and maximum throughput, enabling the system to scale across large fleets without proportional cost increases.
The Outcome
From Manual Monitoring to Autonomous Fleet Intelligence
The platform replaced costly, people-intensive safety monitoring with an automated, always-on intelligence layer. Fleet managers shifted from reactive review to proactive oversight, with alerts surfacing only the events that required human judgment.
Continuous Safety Monitoring
ML-powered detection runs 24/7 across the entire fleet, with zero gaps in coverage and zero manual review required for routine operations.
Reduced Operational Costs
Automation replaced manual monitoring processes, cutting the labor cost of fleet safety management significantly.
Full Video Intelligence
Live streaming, event-based capture, and indexed playback gave fleet managers complete visibility, both real-time and historical.
Improved Safety Outcomes
Real-time alerting enabled faster response to unsafe behavior, reducing incident rates across the client's vehicle base.
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