Case Study: Foodpairing AI accelerates product development with Ontotext GraphDB

Foodpairing AI reduces R&D costs by 25% with Ontotext GraphDB

Foodpairing AI, a Belgian data and business intelligence company, sought to revolutionize product development for CPG companies by predicting successful consumer products. To achieve this, they needed to integrate a multitude of diverse data from ingredients, products, recipes, and social media into a common framework for deeper insights. They partnered with Ontotext to address this challenge.

The solution was the Foodpairing Knowledge Graph, powered by Ontotext GraphDB. This semantic knowledge graph integrated over 20K ingredients, 3M products, and 10M recipes. The implementation delivered substantial results, including an 80% reduction in the time needed to calculate novelty in ingredient combinations, a 70% reduction in cross-team coordination time, and a 25% reduction in R&D costs through optimized resource allocation.

Foodpairing AI