RAG as a Service
Document grounding · No-code setup · ₹ pricing · India-hosted · DPDP-ready
Upload your documents. We handle chunking, embeddings, vector storage, and retrieval. You get a single API that returns answers grounded in your own data — no vector database, no ML pipeline, no ML team required.
How It Works
- 1Sign up and describe the agent you want to build
- 2Upload your documents (PDF, TXT, or CSV)
- 3We index and ground your documents automatically
- 4We issue you a metered API key
- 5Call the API from any app — usage is tracked and billed per plan, automatically
For Developers & Product Teams
An endpoint that drops into any stack. No ML infrastructure to build, scale, or operate.
- • Metered API keys
- • Per-key rate limits
- • Usage analytics
- • Self-hostable
For Indian SMBs & Regulated Teams
A grounded assistant on your own documents, live in an afternoon, hosted in an India region you control.
- • Document grounding
- • No-code setup
- • ₹ pricing
- • India-hosted & DPDP-ready
What's Under the Hood
Smart Chunking
Documents are split into overlapping chunks that preserve meaning across boundaries.
Isolated Knowledge Bases
Every model gets its own vector store — no data leakage between models.
Fast Retrieval
The most relevant passages are retrieved and injected into context automatically, for every query.
India-Hosted & DPDP-Ready
Built for teams whose data needs to stay in India — see our Privacy Policy.
Frequently Asked Questions
What is RAG as a service?
RAG (Retrieval-Augmented Generation) as a service means we run the entire document-grounding pipeline for you — upload, chunking, embeddings, vector storage, and retrieval — behind a single API. You don't need to stand up or maintain a vector database like Pinecone or Weaviate, or write chunking and embedding code yourself.
How is this different from building my own RAG pipeline?
Building your own pipeline typically means managing a vector database, writing document chunking logic, handling embeddings, and maintaining retrieval infrastructure — work that usually needs a dedicated ML engineer. With FineTune Studio, you upload a document and call one API; we handle the rest.
What file types can I upload?
PDF, TXT, and CSV documents are supported today. Files are automatically chunked and embedded so your AI model can retrieve the most relevant passages for each query.
Is my data secure and DPDP compliant?
Each model gets its own isolated knowledge base with no data leakage between models. We're hosted on Indian infrastructure and built with India's Digital Personal Data Protection Act, 2023 in mind — see our Privacy Policy for details.
Do I need an ML team to use this?
No. The whole point of RAG as a service is removing that requirement — describe your agent, upload your documents, and you have a grounded API endpoint, typically in minutes, not sprints.