Superlinked has developed the first VectorOps platform for building personalised software with large language models
Superlinked is a compute framework for turning data into vector embeddings boosting retrieval quality & control in Recommender Systems, Gen AI applications, Fraud detection and more.
The framework supports the fusion of custom and pre-trained embedding models, offers control over the retrieval objectives and boosts data engineering efficiency.
Superlinked was built by ex-Google and Mastercard ML Engineers and an ex-McKinsey COO. Vector embeddings, in the context of machine learning and data analysis, refer to a mathematical representation of objects or features in a multi-dimensional space. Each object or feature is represented as a vector, which is a set of numerical values.
The key idea is to capture the relationships and similarities between objects by placing them in a space where similar objects are closer together. In the case of Superlinked, the framework uses vector embeddings to transform data into these numerical representations.
This process enhances the quality and control of retrieval in various applications, as mentioned, such as Recommender Systems, General AI, and Fraud detection.