Managed RAG API for AI agents
index4.ai is a fully managed RAG API and RAG backend for AI agents and applications. Upload documents, and index4.ai handles parsing, structure-aware chunking, embeddings, vector storage, hybrid search and reranking. Query over REST, the Python SDK or the hosted MCP server and receive prompt-ready context for your own LLM. No vector database, retrieval infrastructure, hosted chatbot or bundled LLM is required.
In the language of the market, that makes index4.ai RAG as a Service — more precisely, a managed context engine: it doesn't stop at ranked chunks, it assembles the context your model reads.
What a managed RAG API is
Managed RAG API: an API that runs ingestion, parsing, chunking, embeddings, index storage and retrieval as one service, so developers can add retrieval-augmented generation to their own application or agent without operating a retrieval stack.
The alternative is to build the RAG pipeline yourself: a parser, a chunking strategy, an embedding model, a vector database, keyword search, fusion, a reranker, and the glue that turns hits into a prompt. A managed RAG backend replaces that whole RAG infrastructure with one API — a drop-in RAG pipeline you call instead of maintain.
index4.ai is one implementation of this architecture, and it is deliberately scoped: it ends at retrieval. Generation stays with your model.
What index4.ai handles — the end-to-end RAG pipeline
| Stage | index4.ai | You |
|---|---|---|
| Ingestion and parsing | Managed — files or URLs, PDFs, Office documents, web pages | Upload |
| Chunking | Structure-aware (structural chunking): headings, sections, lists and tables stay intact (PrimeCut) | — |
| Embeddings | Managed | — |
| Vector storage | Managed — no vector DB to provision | — |
| Keyword (BM25) retrieval | Yes | — |
| Semantic (dense) retrieval | Yes | — |
| Hybrid search | Yes — fused into one ranking | — |
| Reranking | Yes, on the advanced retrieval tier | Pick a tier per query |
| Context assembly | Prompt-ready, token-budgeted, with sources (Context API) | Set a token budget |
| Answer generation | No — bring your own LLM | Your model, your prompt |
| Built-in chat UI required | No | Your app or agent |
| Interfaces | REST API, Python SDK, MCP server | Pick one |
Bring your own LLM — no chat required
index4.ai does not require a chat application or an index4.ai-hosted LLM. It returns retrieval context; any model or agent framework can consume it. That is what separates a RAG backend from a chatbot platform:
RAG backend (index4.ai): documents → index4.ai → prompt-ready context → YOUR model / agent
Chatbot platform: documents → provider → provider's model → provider's chat UIUse it from your own backend, from an agent, or straight from an AI assistant through the MCP server — the same managed retrieval API serves all three.
Retrieval backend for agents
Agents need a knowledge API they can call as a tool — agentic retrieval over your own documents. index4.ai exposes the same retrieval over MCP, so Claude, Cursor or any MCP client can search your documents and get back passages with their sources. Connect it once — the hosted server needs no code of your own; it fetches documents by upload or URL, not from your local disk. See Use index4.ai in your AI assistant or the MCP server reference.
Managed RAG vs. building it yourself
| Build your own RAG pipeline | index4.ai (managed RAG backend) | |
|---|---|---|
| Components to run | Parser, chunker, embedding model, vector DB, keyword index, reranker, glue code | One API |
| Chunking quality | Fixed-size or recursive splitting unless you invest | Structure-aware chunking out of the box |
| Context for the LLM | You assemble, order and trim hits | Prompt-ready, token-budgeted context |
| Model choice | Any | Any — bring your own LLM |
| Ops | Index sizing, re-embedding, upgrades | Managed |
| Full control of every layer | Yes | Use PrimeCut chunks in your own stack instead |
Build it yourself when you need to own every layer of the stack. Use a managed RAG API when retrieval is infrastructure you need, not a product you want to maintain.
Retrieval as a Service
"Retrieval as a Service" is sometimes used for the same idea. In that sense index4.ai is a retrieval service for documents: managed document retrieval for LLMs, with the output shaped as context rather than as a raw hit list.
Pricing
Monthly plans with ingestion and query quotas, plus a free tier. index4.ai doesn't run your model, so LLM costs stay with your own provider. See pricing.
Common questions
What is a managed RAG API? A managed RAG API runs the retrieval side of retrieval-augmented generation as a service: document ingestion, parsing, chunking, embeddings, vector storage and retrieval. You upload documents and query over an API; the service returns context for your own LLM, and you operate no retrieval infrastructure.
Is index4.ai a RAG as a Service platform? Yes. index4.ai is a fully managed RAG backend — more specifically, a managed context engine. It handles ingestion, structure-aware chunking, embeddings, vector storage, hybrid retrieval and reranking, and returns prompt-ready context over REST, a Python SDK or a hosted MCP server.
Do I need a vector database to use index4.ai? No. Embeddings and vector storage are managed by index4.ai. There is no vector database, embedding pipeline or retrieval stack to operate.
Can I bring my own LLM? Yes. index4.ai stops at retrieval: it returns prompt-ready context that you pass to any LLM or agent. It does not generate the final answer and does not require a hosted chatbot or a bundled model.
Does index4.ai include a chatbot? No chatbot is required. index4.ai is backend infrastructure for your own application or agent; you keep the model and the user experience.
Next
- Context API — what "prompt-ready context" means and why top-k chunks aren't enough
- Quickstart — ingest a document and run a hybrid search with curl
- RAG as a Service explained — the category, evaluation criteria and trade-offs