Too Good To Go Uses Mistral on AWS to Cut Store Churn
Too Good To Go built a Mistral AI solution on Amazon Bedrock to diagnose partner churn. The food-waste marketplace, which counts nearly 200,000 active stores and over 100 million registered users, had no efficient way to run root cause analysis on store departures. According to Amazon Web Services, Inc., the company mined its sales team's store interaction logs to find out why suppliers stopped listing surplus food.
The problem was distribution, not product. Stores were leaving silently. Existing processes could not surface the patterns at scale. Feeding thousands of monthly interaction records into Mistral's large language models gave Too Good To Go a categorized read on the friction points driving churn.
The result was actionable: product improvements and a more tailored partner retention approach. Watch whether this model, French-built LLM inference applied to marketplace health data, becomes a template for European platforms managing large supplier networks. Mistral's commercial footprint is expanding through exactly these cloud-embedded, outcome-specific deployments.