Unstructured

Open-source document parsing framework from IBM with 97.9% accuracy in complex table extraction and excellent text fidelity. Self-hostable solution for converting PDFs, spreadsheets, and scanned images into structured data for RAG pipelines.

High-performance document parsing service by LlamaIndex that consistently processes documents in about 6 seconds regardless of size. Returns rich Markdown and optional HTML tables with wide format support through hosted API.

A premium vector database capability within the DataRobot Generative AI platform that stores chunked unstructured text and their embeddings for retrieval-augmented generation (RAG). Users can create vector database objects, connect supported data sources from the DataRobot Data Registry, configure embeddings and chunking, and attach these vector databases to LLM blueprints in the playground to ground model responses in proprietary data.

A compound AI system for parsing, chunking, enriching, and storing unstructured documents at scale, trained on 80k+ enterprise documents and delivering up to 6x better accuracy and 5x cost savings compared to alternative systems.

A fully managed service within Amazon Bedrock that automates the retrieval-augmented generation (RAG) workflow by ingesting unstructured and structured data, converting it into embeddings, and storing them in supported vector databases. It enables grounding generative AI responses with enterprise data without manual orchestration.

A powerful RAG application platform delivering OpenAI-compatible serverless inference APIs for top open-source LLM models. Offers specialized batch processing for large-scale async AI workloads and document extraction capabilities designed for RAG applications, balancing cost-efficiency with high performance.