Transforming Unstructured Content into AI-Ready Context at Scale

Unstructured and MongoDB have partnered to help teams unlock the full value of their data by making it easier to build modern AI applications. Together, we offer an end-to-end pipeline for turning unstructured documents into structured, retrievable knowledge. The result is content that is ready for vector search, intelligent agents, and context-aware copilots.

Integration Capabilities

Getting high-quality context from real-world documents is a critical challenge in AI system development. Unstructured addresses this by converting complex files into clean, structured data enriched with semantic, visual, and positional signals. These enriched chunks are ideal inputs for retrieval-based systems.

MongoDB Atlas offers a scalable and flexible platform for storing and querying these outputs. With support for both vector and metadata search, it ensures fast and accurate results across large and evolving datasets.

Developer Use Cases

This partnership is built for teams creating the next generation of intelligent applications. By combining Unstructured with MongoDB’s flexible data infrastructure, developers can spend less time managing pipelines and more time building systems that are fast, explainable, and context-aware.

Looking to get started? We’d love to hear about your use case.