Enterprise-Grade RAG Platform

Turn your knowledge into
AI-powered answers

Upload documents, ask questions, and get instant cited answers— grounded in the information you trust, built with state-of-the-art vector search.

Cited answers Grounded in your documents Multi-model AI
Target for solar capacity by 2030?
280 GW by 2030, per the national frameworkDoc A, pg 12.

Your AI-Powered Research Partner

Step 01

Upload your sources

Upload PDFs, policy documents, circulars, and reports. Nano AI RAG will summarize them and make interesting connections between topics, all powered by advanced retrieval techniques.

PDFSolar_Mission_Policy.pdf
Step 02

Instant AI Indexing

With all your sources in place, the system goes to work. Documents are automatically chunked, embedded, and mapped into a high-dimensional vector database for semantic understanding.

Step 03

Chat with your sources

Ask questions and receive instant, accurate answers. Every response comes with a direct citation pill, allowing you to instantly jump to the exact page and paragraph in the original document.

What is the target for solar capacity by 2030?
The target for solar capacity is 280 GW by the year 2030, according to the national renewable energy frameworkDoc A, pg 12.
Flexible deployment

Cloud-ready. Self-hosted when you need it.

Start in minutes on our managed cloud—or run the full stack on your infrastructure when data residency and control come first.

Managed cloud

We host and operate Nano AI RAG so your team can upload documents and start asking questions without standing up servers.

  • Instant access—no infra to provision
  • Automatic updates and scaling
  • Enterprise security, managed for you
Try cloud now

Self-hosted

Deploy on your servers, private cloud, or air-gapped environment. Your documents and vectors never leave your network.

  • Full data residency and control
  • On-prem, VPC, or private cloud
  • Fits your security and compliance stack
Talk about self-hosting
Model flexibility

Pick the AI model that fits your policy

Nano AI RAG is model-agnostic. Use Sarvam AI when data should stay in India, or switch to Claude, OpenAI, or Gemini—same RAG stack, citations, and controls either way.

  • Multilingual by design—ask in English, Hindi, or Hinglish and get grounded answers from the same documents.
  • Swap models without re-building
  • Match residency to each deployment
  • Same grounding on every model
Available models
  • ClaudeAnthropic
  • OpenAIGPT models
  • GeminiGoogle