DehazeLabs builds ML-powered routing and dispatch systems, computer vision for dock and warehouse operations, and logistics data platforms. Reference deployment: Cargomatic — AI/ML for routing optimization and container unloading.
Logistics AI sits at the intersection of real-world physical operations and complex optimization. Routing models that account for driver behavior, real-time traffic, and load characteristics. Computer vision that runs at dock cameras and yard gates, not just in a lab. Data pipelines that normalize trip telemetry, TMS records, and GPS feeds into a format where models can actually train and operate.
Our reference engagement is Cargomatic, the US logistics marketplace, where we built AI/ML systems for freight routing optimization and container unloading sequencing. The platform handles real-world dispatch complexity — partial loads, carrier availability, dwell-time prediction — not just textbook routing on clean data.
Routing models trained on your historical trip data. Real-time re-routing on traffic and availability signals. Carrier-load matching that optimizes across hard and soft constraints. Exception handling for the edge cases where rule-based systems break down.
Camera-based monitoring for dock operations — container identification, loading sequence automation, dwell time tracking, exception alerting. Built for the hardware that's already in the facility, not a clean-room deployment scenario. See how we approach production CV deployment end-to-end.
Ingestion and normalization of TMS, GPS, ERP, and telemetry data into a unified operational schema. The data engineering foundation without which routing models can't train reliably.
ETD prediction, delay detection, and exception alerting for operational teams and customer-facing SLAs. Models that improve as operational data accumulates.
Tell us about your routing complexity, dock operations, and the data you have today. We'll tell you what's feasible and what it would take to build it.