Case Study: MediaHound achieves faster product discovery and recommendations with Neo4j

MediaHound - Customer Case Study

MediaHound needed a flexible way to support connected product data for a business-to-business marketplace, where millions of items had to be tied to manufacturers and enriched by distributors with pricing, lead times, and other inventory details. The team chose Neo4j to model these relationships and make it easier to explore features like product recommendations without relying on machine learning expertise.

Using Neo4j, MediaHound was able to store and query highly connected marketplace data efficiently, making it straightforward for distributors to match inventory and add key business information. The result was a more productive development process, faster onboarding for a junior developer, and an easier path to building recommendation-style features, showing clear value from Neo4j’s graph database approach.

MediaHound

Matthias Sieber

Senior Software Engineer