Want more accurate AI Agents? Give them better data.

Accurate AI agent performance hinges not just on model quality but on the effectiveness of data retrieval systems that supply the model with pertinent information. While developers often rely on general-purpose semantic search endpoints expecting models to autonomously gather necessary data, this approach can yield inconsistent outcomes due to insufficient context retrieval. The solution lies in building structured retrieval systems that differentiate between operational data and unstructured knowledge, utilizing semantic search as a foundation for retrieval augmented generation (RAG) systems. By enriching vector data with structured metadata, particularly in complex domains like legal documents, agents can more precisely access relevant data, improving context accuracy and response quality. Tools like Vectorize streamline this process by allowing developers to define document schemas and metadata, thus enhancing semantic search results and enabling efficient, scalable AI projects.