What is Ragie?
Ragie is a context engine that provides purpose-built APIs for indexing, retrieval, parsing, and entity extraction to give AI agents, assistants, and apps the right context. It supports multimodal data (text, PDFs, images, audio, video), native connectors (Google Drive, Notion, Confluence, Slack), hybrid search, MCP server, partitions, and enterprise-grade security (SOC 2, GDPR, HIPAA). It is used for legal research, productivity assistants, and sales tech.
SpecificationsAI-estimated
Key Features of Ragie
Use Cases for Ragie
Legal research
Optimizes legal research with high recall and precision, enabling 5-10x faster drafting.
AI assistants
Powers AI assistants with company-specific knowledge from Google Drive, Notion, Confluence, etc.
Sales tech
Enhances sales tech with precise retrieval from product data, customer interactions, and reviews.
How to use Ragie?
Connect
Upload files via API or sync from Google Drive, Notion, Slack, and more using pre-built connectors.
Ingest
Ragie parses, chunks, and indexes content across text, PDFs, images, audio, and video.
Deploy
Get structured data, extracted entities, or retrieved context via API.
Pros & Cons of Ragie
Pros
- Handles full retrieval pipeline (vector, keyword, summary indexes)
- Agentic OCR extracts structured elements from any document with bounding boxes
- Plain language entity extraction
- Multimodal support including video
- Native connectors for popular tools
- Hybrid search for accurate context
- MCP server for AI tool integration
- Data isolation via partitions
- Enterprise-grade security and compliance
- Flexible deployment (cloud, VPC, on-prem)
- 99.9%+ uptime
- Highly praised developer experience
- Fast time-to-value (e.g., 3 weeks vs 3 months)
Cons
- Pricing not publicly available
- May be overkill for simple RAG use cases
- Dependency on third-party service for core RAG infrastructure
Pricing Overview
View full pricing →Details below.
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