Accurately automating automotive repair recommendations
AutoCorp is a fast growing startup that helps streamline the automotive repair process by providing mechanics at auto-repair shops with precise diagnostic recommendations. This helps mechanics focus more attention on troubleshooting harder issues and on up-selling opportunities.
In order to rapidly get to market, AutoCorp approached Greenlake to develop a system that provided accurate recommendations regarding maintenance services for various types of automobiles entering an auto-repair store as early as possible.
Given our client's domain expertise in the auto-repair space, we collaborated closely with their team in order to identify the salient features from their database of repair parts and history of automotive repairs. Our team then created and compared several models that could recommend the most likely repair parts given information about any vehicle brought in for repair. These methods were evaluated via a top-K metric specifically suited for such problems. The best among the solutions was then successfully validated on an un-seen dataset of customers.
The solution developed by Greenlake yielded a better-than-human performance. Hence, the management at AutoCorp gave the go-ahead to to put the model into production.
Hence, the engagement with Greenlake reduced the time to market for AutoCorp by several months, allowing the client to focus on rapid business development.