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Agentic AI Data Platform for seamless AI agent data integration.
Vectorize is a powerful Agentic AI Data Platform designed to bridge the gap between AI agents and the data they need to operate effectively. It provides a unified, secure environment for accessing and processing both structured and unstructured data, ensuring that AI agents can retrieve information with precision and speed.
The platform's core strength lies in its agent-first approach to data retrieval. Unlike traditional methods, Vectorize offers features like multimodal extraction (including from complex PDFs, diagrams, and transcriptions using their Iris vision model), custom metadata filtering for precise data selection, and configurable pipelines that support various data sources like Google Drive, S3, and more, all without requiring custom code. This ensures that AI agents receive clean, relevant data, minimizing noise and guesswork.
Vectorize is ideal for developers, data scientists, and enterprises aiming to build sophisticated AI applications, such as intelligent chatbots, advanced RAG systems, and automated workflows. Its ability to handle diverse and complex data formats, coupled with its robust API and SDKs, makes it a versatile tool for enhancing AI agent performance. The platform also includes built-in evaluation tools to benchmark embedding models and chunking strategies, allowing for optimized data retrieval before deployment. With Vectorize, teams can unlock the full potential of their AI agents by providing them with a reliable and intelligent data layer.
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Connects AI agents to structured and unstructured data for precise retrieval.
Extracts information from complex documents like PDFs, diagrams, and charts using advanced vision models.
Allows agents to filter and retrieve data based on custom-defined metadata fields.
Set up production-ready data pipelines with support for cloud storage and other sources without custom code.
Provides an API with re-ranking, query rewriting, and metadata-aware search for accurate agent queries.
Includes tools to evaluate and benchmark embedding models and chunking strategies for optimal performance.