Retrieval-Augmented Generation · Grounded on your data

RAG on your own data, grounded inside your perimeter.

Retrieval-augmented AI that answers from your own documents — accurately, with citations, and without a single file leaving your environment. The ingestion, embeddings, and vector index all live inside your tenant.

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The risk

An LLM doesn't know your business — and guesses when it doesn't.

Out of the box, a model invents plausible-sounding answers about your policies, contracts, and customers. The usual fix — a public RAG tool — sends your documents to a third-party embedding API and a vector database you don't control. For a regulated enterprise — under GDPR, HIPAA, India's DPDP, or RBI rules — that's the same exposure you were trying to avoid. Private RAG grounds the model in your data while keeping every document, embedding, and index inside your perimeter.

How private RAG works

Four stages. All inside your walls.

Ingestion

Your documents, wikis, tickets, and databases parsed, chunked, and embedded — entirely inside your environment.

Retrieval

Hybrid semantic and keyword search over your private index. The right passages surfaced for every query.

Grounded answers

Responses built only from your retrieved sources — not the model guessing from stale training data.

Citations & guardrails

Every answer traces back to its source. Access controls and PII filters enforced at query time.

Who it's for

Built for the world's regulated enterprises.

Banking & Financial Services

Policy, compliance, and product manuals answered instantly — with citations your auditors accept.

Healthcare

Clinical protocols and patient records queried privately, aligned to DPDP and HIPAA.

Government & Public Sector

Decades of policy and records made searchable on sovereign infrastructure, in your jurisdiction.

Legal & Professional

Contracts, case files, and matter history searched without privilege ever leaving your control.

Pharma & Life Sciences

Research, trial data, and regulatory dossiers surfaced from your own corpus, fully owned.

Manufacturing & Energy

SOPs, maintenance logs, and engineering docs turned into answers on the plant floor.

Compliance & sovereignty

Retrieval that stays on the right side of your mandates.

Because ingestion, embeddings, and the index never leave your environment, data residency holds by construction. We architect every retrieval pipeline to slot into the regulatory posture your auditors already expect — access control, citation trails, and PII handling built in from day one.

GDPRHIPAASOC 2ISO 27001CCPADPDP Act 2023RBI Localization
Under the hood

Production retrieval. Not a notebook demo.

Open-weight embedding and language models, a vector store on your own infrastructure, hybrid retrieval and re-ranking, evaluated and observable — the same stack we run in production, deployed entirely within your tenant.

pgvectorQdrantvLLMLlamaIndexBGE EmbeddingsElasticsearchYour VPC
What you get

Answers you can put in front of a regulator.

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Grounded in your sources

Every response cites the document it came from. No hallucinated policy, no invented numbers.

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Your data never leaves

Ingestion, embeddings, and the vector index all live inside your tenant — never a third-party API.

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Access-aware retrieval

Users only ever retrieve what they are cleared to see. Your existing permissions enforced at query time.

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Always current

Re-index on your schedule, so answers reflect today’s documents — not a model’s training cut-off.

Questions

The things enterprises ask first.

Where do our documents and embeddings live?

Inside your own environment. Ingestion, the embedding model, and the vector index all run within your cloud tenant or data centre. Your documents are never sent to a third-party embedding or vector API.

How do you stop the model hallucinating?

The model answers only from passages retrieved from your data, and every answer carries citations back to the source document. If the answer is not in your corpus, it says so rather than inventing one.

Can it respect our existing access permissions?

Yes. Retrieval is access-aware — each query is scoped to what the user is cleared to see, so confidential documents never surface to people who should not have them.

Which data sources can you connect?

SharePoint, Confluence, Google Drive, S3, relational databases, ticketing systems, and most document stores. We build connectors to your sources and keep the index in sync on your schedule.

Make your knowledge answerable.

Tell us your sources and constraints — jurisdiction, sector, infrastructure — and we'll scope a private RAG system that fits.

Talk to our team →