What does Ragie do and what problem does it solve?
Ragie is a fully managed RAG-as-a-Service platform that provides a context engine for AI agents, assistants, and apps. It solves the problem of building complex AI data pipelines by handling indexing, retrieval, and data integration automatically, allowing developers to focus on building AI applications rather than managing the underlying infrastructure. The product offers purpose-built APIs for context needs, including indexing, retrieval with citations, and multimodal support.
What features does Ragie offer?
Ragie offers a comprehensive set of features for AI context management, including an Agentic OCR parser, entity extraction, and native connectors for data sources like Google Drive and Slack. Its core features are multimodal support (text, PDFs, images, audio, video), hybrid search combining vector, keyword, and summary indexes, a context-aware MCP server, and data partitioning for security. It also includes reranking, webhooks, and recency bias.
What is Ragie used for and in what situations?
Ragie is used for building context-powered AI applications by providing a unified pipeline to ingest, parse, and index content from various sources. It is used in situations where developers need to give AI models access to a knowledge base, such as creating enterprise search, document processing, or digital marketing tools. It supports multimodal data (text, PDFs, images, audio, video) and integrates with data sources like Google Drive, Notion, and Slack.
Who is Ragie for?
Ragie is for developers and teams building AI applications, from startups to large enterprises. It is specifically designed for those who need to build context-powered AI systems, such as agents, assistants, and apps, and are looking for a managed service to handle data integration and retrieval rather than building the infrastructure from scratch.
What is Ragie?
Ragie is a fully managed RAG-as-a-Service platform and the 'Context Engine' for building AI agents, assistants, and applications. It provides APIs for indexing and retrieving context from multimodal data sources to power AI systems.
5 of 6 research questions are answered for this product. The rest need source evidence we have not collected yet, so they are left unanswered rather than guessed.