Vectorize provides an agentic AI data platform that powers intelligent retrieval for building AI agents and copilots. The platform offers advanced agent memory capabilities, allowing agents to recall facts and learn from experience for improved consistency. It also features context engineering tools to process unstructured data and deliver relevant context to agents via optimized pipelines and APIs.
Funding
$3.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations struggle to efficiently leverage unstructured data trapped in various repositories for retrieval-augmented generation (RAG) applications. Extracting, preparing, and maintaining AI-ready vector embeddings from diverse knowledge sources is a complex and time-consuming process. This complexity hinders the ability to create accurate and up-to-date search results for generative AI applications.
Solution
Vectorize provides a cloud-based platform that streamlines the transformation of unstructured data into optimized vector search indexes, purpose-built for RAG pipelines. The platform automates the ingestion, chunking, embedding, and evaluation of data from various sources, including file systems, knowledge bases, and collaboration platforms. Vectorize analyzes multiple chunking and embedding strategies in parallel to identify the optimal configuration for a given dataset. The platform then deploys a real-time vector pipeline that automatically updates as source data changes, ensuring that RAG applications always have access to the most current and relevant information. By automating the vectorization process, Vectorize enables organizations to quickly build and deploy production-ready RAG pipelines that leverage the power of large language models (LLMs) on their own data.
Target Audience
Vectorize targets AI developers and enterprises seeking to build and deploy RAG applications that leverage unstructured data, including those building AI copilots, question answering systems, and content automation tools.
Features
- Connectors for ingesting data from various sources, including content management systems, file systems, CRMs, and collaboration tools
- Automated extraction of natural language from unstructured documents
- Parallel evaluation of multiple chunking and embedding strategies
- Recommendation engine for selecting the optimal vector configuration
- Real-time vector pipelines that automatically update as source data changes
- Integration with popular vector databases
- RAG evaluation engine for identifying the best vectorization strategy for unique data
- Cloud-native architecture designed for scalability and performance