# Ragie

> Managed context engine and RAG-as-a-Service that ingests, parses, indexes, and retrieves data for AI agents, assistants, and apps via API.

Ragie is a fully managed RAG-as-a-Service platform from Ragie, Corp that gives AI agents, assistants, and applications context from their own data. It combines document parsing, indexing and retrieval, and entity extraction behind APIs and SDKs. The homepage also carries an announcement that Ragie joins Ticketmaster.

## What It Is

Ragie is a context engine for developers who want to avoid building and maintaining their own retrieval pipeline. Content is uploaded through the API or synced from sources such as Google Drive, Notion, Confluence, and Slack. Ragie then parses, chunks, and indexes text, PDFs, images, audio, video, and presentations. Applications get back structured data, extracted entities, or retrieved context through the API.

## How the Pipeline Works

The workflow has three steps: connect, ingest, and deploy. Ragie builds vector, keyword, and summary indexes and combines them for hybrid search. It also offers LLM reranking, recency bias, webhooks, and deep search. Ragie Parse uses agentic OCR to extract tables, forms, charts, and key-value pairs with bounding boxes for traceability. Entity extraction lets users describe in plain language what to pull from documents.

## Agents, MCP, and Isolation

A context-aware MCP server gives agents permissioned access to a knowledge base through existing AI tools, and agentic retrieval supports multi-step reasoning. Partitions isolate data by tenant, workspace, or customer. Connectors can be embedded in an app so end customers can connect their own data.

## Security and Deployment

Ragie states it is SOC 2 Type II, GDPR, HIPAA, and CCPA compliant, uses AES-256 encryption at rest and TLS in transit, and does not use customer data for training. It lists cloud, VPC, and on-prem deployment options.

## Features
- Index and retrieval with vector, keyword, and summary indexes
- Hybrid semantic and keyword search
- Agentic OCR document parsing with bounding boxes
- Entity extraction from plain-language instructions
- Multimodal ingestion of text, PDFs, images, audio, and video
- Native connectors for Google Drive, Notion, Confluence, Slack
- Context-aware MCP server
- Partitions for multi-tenant data isolation
- Reranking, webhooks, and recency bias
- Agentic retrieval for multi-step reasoning
- Flexible deployment in cloud, VPC, or on-prem

## Integrations
Google Drive, Notion, Confluence, Slack, Claude Code, Cursor

## Platforms
API, WEB, DEVELOPER_SDK

## Pricing
Freemium — Free tier available with paid upgrades

## Links
- Website: https://www.ragie.ai
- Documentation: https://docs.ragie.ai/docs/getting-started
- EveryDev.ai: https://www.everydev.ai/tools/ragie