Case Study: Later cuts campaign planning from days to hours with SurrealDB

Later cuts campaign planning from days to hours with SurrealDB

Later, a social media and influencer marketing platform, faced a challenge in efficiently matching brands with the right content creators for campaigns. Their previous process relied on manual work and rigid filters, which was time-consuming and lacked nuance. As their AI ambitions grew, their fragmented infrastructure of relational databases struggled with the deeply interconnected, semi-structured nature of creator data, hindering their need for speed, accuracy, and scale.

Later chose SurrealDB to build a context-aware knowledge graph that unifies graph relationships, document flexibility, and vector search. This solution enabled AI-powered semantic search and graph traversal, allowing for dynamic creator discovery. The results were significant: campaign planning was reduced from days to hours, and creator discovery went from hours to minutes. This led to more activations per quarter and higher win rates and margins for Later.

Later