Ragie Connect
Build RAG applications on your user data
Ragie Connect launched on January 6, 2025 with 353 votes, #77 of 6,142 launches that month and more than 99% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Ragie Connect is a platform that enables developers to build retrieval-augmented generation (RAG) applications using their users' data. It manages authentication and automatically syncs data from sources like Google Drive, Salesforce, and Notion, allowing teams to integrate AI features into products quickly. The tool is designed for developers and product teams who want to add intelligent data retrieval capabilities without building these integrations from scratch.
written from the facts on this page · September 2026
From the sources
Easily embed RAG into your product with Ragie Connect. It handles authentication and automatic user-data syncing from Google Drive, Salesforce, Notion, and many more, so you can easily build RAG on top of your users' data. Ship AI features in minutes.
Ragie is the Context Engine for Agents, Assistants, and Apps — a fully managed RAG-as-a-Service platform with real-time indexing, retrieval with citations, multimodal support, and a free developer tier.
Ragie combines intelligent indexing and retrieval, document parsing, and entity extraction to power your AI with accurate, reliable context. Ragie builds vector, keyword, and summary indexes from your content and handles the full retrieval pipeline, so your agents and assistants always get the right context, fast and at any scale. Ragie's Agentic OCR extracts structured elements from any document, including tables, forms, charts, and key-value pairs, with precise bounding boxes for full traceability. Tell Ragie what to extract in plain language and it automatically pulls structured entities from every document. Precise extraction for when context alone isn't enough. Upload files via API or…from ragie.ai
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Tilores Identity RAG2024 · tilores.io · ▲323Customer data search, unification and retrieval for LLMs

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