Gousto, a UK-based recipe box service, uses Neo4j to create a personalized recommendation engine that suggests recipes based on customer preferences and ingredient similarity. The company's data journey began with external data sources such as Google Analytics and has since evolved into a rich data ecosystem supported by Amazon Redshift, Periscope, Salesforce, Airflow, Snowplough, and Neo4j. Gousto's marketing attribution model helps allocate resources effectively, while its forecasting model determines orders for ingredients based on weather, holidays, and customer trends. The company also uses a warehouse optimization system to efficiently pack boxes, and personalization is crucial in the merchandising process. A hybrid recommendation engine combining collaborative filtering and content-based models, LightFM, was developed to overcome the "cold start" problem and provide more accurate recommendations. Neo4j's graph ontology allows Gousto to capture complex relationships between recipes and ingredients, enabling the creation of similarity scores and inferences from data. The company is now exploring ways to leverage Snowplow data for personalization and AI-based recipe development.