Relevance AI's mission is to accelerate developers to solve similarity and relevance problems through data. As our first step towards helping teams, we started with the data type that all the top tech companies use - vectors, a high dimensional representation of data used to determine similarities between data.
Some important similarity and relevance based problems that can be solved with vectors:
Semantic & unstructured data search
Data deduplication & matching
Zero shot classification
K-nearest neighbors similarity-based regression
and many more
Experimentation-first vector platform
In the vector workflow to solve search and relevance problems, we decided to focus heavily on the foundation of all good solutions - the experimentation stage. Our experimentation-first approach helps users experiment, tune and prototype various vector weightings, configurations, data structures and vector search methods to improve their vectors. For a more in-depth exploration and comparison take a look at our article on experimentation-first vector database.