Ragie
Production-ready RAG-as-a-service for AI developers and startups
About Ragie
Ragie is a fully managed Retrieval-Augmented Generation (RAG) backend engineered to eliminate the operational complexity of building and maintaining custom vector pipelines. It handles document parsing, semantic chunking, embedding generation, vector indexing, and hybrid re-ranking through a single high-performance API endpoint. Instead of configuring separate chunking scripts, vector databases, and re-ranking algorithms, engineering teams connect Ragie directly to their data sources. Ragie continuously keeps embeddings synchronized and provides sub-100ms context retrieval designed specifically for conversational AI assistants and knowledge search engines.
Under the hood, Ragie integrates state-of-the-art document layout models capable of extracting tables, code snippets, PDFs, Notion pages, and Google Docs without formatting degradation. Its retrieval engine blends dense semantic vector search with sparse BM25 keyword matching and cross-encoder re-ranking for maximum recall. Ragie features built-in partition-level access control, ensuring that multi-tenant SaaS applications can isolate tenant data securely while querying a shared knowledge infrastructure.
Create a Ragie account and generate your private API key.
Upload raw documents (PDFs, Word docs, spreadsheets, or text files) via the Ragie REST API or Webhook connectors.
Ragie automatically parses layout structures, generates dense embeddings, and indexes chunks across its search cluster.
Query the Ragie retrieval endpoint with user prompts to receive ranked, highly relevant context snippets.
Inject the retrieved context directly into your LLM prompt payload to generate grounded, hallucination-free answers.
Capabilities & Features
Common Use Cases
rag-pipelines
enterprise-search
ai-customer-support
document-intelligence
knowledge-bases
Frequently Asked Questions
What is Ragie and how does it simplify RAG?
Ragie is a managed RAG backend that takes care of document parsing, semantic chunking, vector indexing, and hybrid re-ranking so developers can implement AI knowledge search in minutes.
Does Ragie support table extraction from PDFs?
Yes, Ragie uses layout-aware parsing models that preserve complex tabular data and multi-column document formats with high fidelity.
Is Ragie suitable for multi-tenant SaaS applications?
Yes, Ragie supports partition-level isolation, allowing you to segment data by customer organization or user ID securely.
Free Plan
Free tier with up to 10,000 document partition chunks and standard hybrid search
Paid Plan
Pay-as-you-go pricing from $0.10/1k pages indexed and dedicated enterprise clusters
Pros & Cons
Eliminates vector database and chunking infrastructure setup overhead
Advanced document layout parser handles complex tables and multi-column PDFs
Hybrid retrieval combining semantic embeddings, BM25, and cross-encoder re-ranking
Built-in multi-tenant partition security for SaaS products
Sub-100ms query retrieval latency SLAs
Proprietary hosted service without a standalone offline deployment mode
High-volume enterprise indexing costs scale with total page volume
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