Building a self-service interface for an AI recommendation engine
Our client, Froomle, is developing a cutting-edge recommendation engine as-a-service for clients in the news and media industry. They needed a visual interface to empower their clients with insights into the configuration and monitoring of the recommendation engine. Froomle tasked us with conceptualizing and building a comprehensive self-service interface. This case study delves into the details of our collaboration and the approach.

Challenge
Froomle had built an impressive recommendation engine, but they recognized the need to enhance it with a user-friendly interface to truly unlock its potential as a self-service product for their clients. They approached us to create a visualization layer on top of their existing technology, making it more accessible and insightful.
Solution
We began by researching customer journeys, focusing on various user personas that we defined collaboratively with Froomle. This research culminated in a detailed, interactive prototype that ensured buy-in from the entire Froomle team. We then implemented the front-end application, seamlessly integrating it with their existing APIs.
Approach
We formed a blended product team, combining the expertise of a product manager and technical support from Froomle with an engineering manager, a designer, and a team of skilled software engineers from our side. We established an efficient sprint-based operating model, streamlined deployment pipelines, and fostered a dynamic delivery culture.

The Froomle team approached us with clear product objectives, yet they gave us a lot of autonomy to define the most efficient way to reach those goals. Through our systematic approach, we were able to deliver beyond expectations in a shorter timeframe than initially foreseen.
We wish the whole Froomle team the very best of luck as they are growing across Europe. We’ll be happy to keep supporting you in every way possible.


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