Ragie

Ragie acts as a context engine that combines indexing, retrieval, document parsing, and entity extraction to supply AI with reliable context. It targets teams building context-powered AI, including agents, assistants, and apps, and emphasizes real-time updated content and structured data that drives accurate responses.

The workflow begins with connecting data sources via API or pre-built connectors, ingesting content, and deploying structured data or retrieved context for applications. Ragie provides a hosted solution that handles parsing, indexing, and serving data through APIs without requiring users to build their own pipeline.

Ragie offers multimodal processing for text, PDFs, images, audio, and video, and aims to keep context clean and accurate regardless of source format. It also provides native connectors and a hybrid search approach to surface relevant results.

Ragie plans include scalable deployment options, tenant-based data isolation, and enterprise-grade security, with multiple deployment modes and connectors.

Use cases

• Legal teams use Ragie to optimize legal research with recall and precision
• Productivity teams use Ragie to enable company-wide instant answers from tools like Google Drive and Notion
• Sales tech teams use Ragie for precise retrieval from product data and customer interactions