The Froomle team has been building an AI recommendation engine as-a-service for multiple years, used by large clients. They were lacking a visual interface for their clients to get insights into the configuration and monitoring of these recommendations. Froomle asked us to conceptualise and build the full self-service interface.
Froomle needed to be able to customize the platform to meet the demands of each customer in their diverse client portfolio. Our goal was to create an intuitive web application that could consolidate all needs into a single self-service interface.
In a first phase we made sure that end-users could generate personalized recommendations based on user-defined personas and sample data. In a second phase we extended the platform allowing for better brand management as well as the technical settings associated with fine-tuning the AI model's hyperparameters.
As I previously managed our customer success activities, I was very aware of the technical and operational complexities in building the self-service portal. The Panenco team quickly grasped our objectives and was able to deliver on our expectations and way beyond.
Head of Product at Froomle
A solution for the interpretability and customization of AI models
To address the first challenge of improving interpretability, we conceptualized an intuitive user interface that allows clients to view and understand the key factors that influence the model's recommendations.
This feature provides transparency into the decision-making process, allowing clients to have more insights in the recommendations made.To tackle the second challenge of customization, we implemented a flexible system that allows clients and customer success to adjust the model's parameters and constraints to better align with the specific business objectives.
This allows Froomle to offer a tailored solution to each client, ensuring that the recommendations generated by the model are optimized for their individual needs.The result of our efforts is a web application that combines interpretability and customization, providing Froomle's clients with a powerful tool for understanding and utilizing their first-party data to generate high-quality recommendations.
A scalable web application for explainable AI
To address the issue of explainability, we gave users the ability to input their own data and observe how the model reacts to it. This allows them to gain a better understanding of the model's behavior and the quality of its outputs. We also implemented the ability for users to tune the model's parameters for increased customization. Throughout the development process, we made sure to prioritize scalability to ensure that the tool could be used effectively by a wide range of users.
Supporting the go-to-market
The self-service portal is actively being rolled out internally at Froomle to support the customer success activities and to all existing customers. We’re assisting the Froomle team in setting up the necessary instruments to monitor success parameters through qualitative and quantitative measurements. We’re curious to see which impact this product will have across the Froomle customer base.
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