Superlinked is a company building the data and compute framework that turns complex, multi-attribute enterprise data into vectors ready for AI-powered retrieval. Most vector search tutorials assume you only embed text, but real applications need to weigh many signals at once: a product's description, its price, its popularity, how recently it was added, and how a specific user has behaved. Embedding only the text and bolting on metadata filters produces brittle, low-quality results. Superlinked's thesis is that these signals should be encoded directly into the vector so that similarity search natively reflects business relevance.
The framework provides composable 'spaces' for different data types — text similarity, numbers, categories, recency, and more — that are combined into a unified embedding. Developers describe their schema and the relative importance of each signal in Python, and Superlinked handles the vector computation and querying logic. The result is retrieval that can answer nuanced queries like 'find affordable, recently launched products similar to this one that users like me tend to buy' without an explosion of hand-tuned filters and re-ranking heuristics.
Superlinked offers an open-source library for experimentation alongside the Superlinked Server, a production deployment that exposes embeddings and querying as a managed service. The company has built integrations and partnerships with vector and data infrastructure players including MongoDB, Redis, Qdrant, Dataiku, and Starburst, positioning itself as the missing computation layer that makes those stores far more useful for ranking, RAG, and recommendation systems.
The company was founded by Daniel Svonava and Ben Gutkovich and raised a $9.5M seed round in March 2024 led by Index Ventures with significant participation from Theory Ventures, plus angels and operators from the data ecosystem. Superlinked targets engineering and data science teams who are frustrated by the quality ceiling of naive vector search and need retrieval that understands the full shape of their data.