Ragie

What is Ragie?

Ragie is a fully managed RAG-as-a-Service platform designed to power AI agents, assistants, and applications with accurate, reliable context. It combines intelligent indexing, retrieval, document parsing, and entity extraction into a single, unified pipeline, eliminating the need to build or maintain complex infrastructure. Ragie supports a wide range of content formats including text, PDFs, images, audio, and video, processing them through one pipeline to deliver clean, accurate context regardless of source.

Key features include Agentic OCR, which extracts structured elements like tables, forms, charts, and key-value pairs with precise bounding boxes for full traceability. Users can also instruct Ragie in plain language to automatically pull structured entities from documents, providing precise extraction when context alone is insufficient. Ragie builds three separate indexes—vector, keyword, and summary—and combines them to surface the most accurate context every time. It offers purpose-built APIs for every context need, including retrieval with citations, reranking, webhooks, recency bias, and deep search.

For data connectivity, Ragie provides pre-built connectors to sync content from Google Drive, Notion, Slack, Confluence, and many more, ensuring context stays current automatically. Users can also upload files directly via API or embed connectors in their own applications for customer data integration. Security is a priority, with tenant, workspace, or customer data isolation to prevent leakage and improve retrieval relevance at scale. Ragie’s context-aware MCP server gives agents permissioned access to knowledge bases through existing AI tools.

Use cases include enhancing customer support chatbots with accurate product documentation, powering research assistants with multimodal data, and enabling enterprise knowledge management with real-time indexing. Technical details: Ragie handles the full retrieval pipeline, from ingestion and chunking to indexing and retrieval, with support for multimodal content and structured data extraction. A free developer tier is available for experimentation and small-scale projects. Ragie is ideal for teams looking to quickly integrate RAG capabilities without operational overhead.

Use Cases

Key Features

fully managed RAG as a Service Agentic OCR extraction multi format document parsing unified indexing pipeline vector keyword summary indexes retrieval with citations pre built data connectors entity extraction reranking and deep search webhooks and recency bias

Who is it for?

AI developers, data scientists, enterprise architects, product managers, knowledge management teams, customer support teams, content managers

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