Neurealm enabled a predictive maintenance solution at the edge for a German truck manufacturer, enabling seamless deployment of ML/DL models on automotive controllers to reduce unplanned fleet downtime.

Business Context

A leading truck OEM had developed statistical and deep learning models for predictive maintenance using offline fleet data, but lacked a repeatable, hardware-agnostic way to deploy these models into vehicles and production pipelines. Challenges included portability across controllers, appropriate packaging of ML models and libraries, and deterministic deployment on embedded automotive systems to deliver real-world value to fleet owners.

Solutions

Neurealm defined a robust framework and playbook for packaging and porting ML/DL models to selected automotive controllers, profiling hardware and software dependencies, and building pipeline components for data transformation and feature synthesis. They prepared the deployment environment (including containers, runtimes, and firmware interfaces) and developed native/cloud interfaces, enabling reliable edge inference and model execution within the vehicle telematics stack.