Assisting with development workstreams, an AI Center of Excellence (CoE), an MLOps framework for backlog, governance of the sandbox and forecasted compute requirements, we helped the client scale to several additional models in production — supporting more than 7,500 vehicles on the road.
The outcome: Reduction of tire-related downtime by nearly 50%
What began as a single productionized model scaled to several models powering a next-gen automated tire management platform — a platform ultimately proven to reduce tire-related downtime by nearly half.
As a result of this work, our client became the first in its industry to implement real-time analysis at scale for a commercial fleet. Today, the company is using the platform to reduce tire costs, increase labor productivity, and keep fleets running more efficiently and safely than ever.