If you want AI that actually knows your company’s information — not just generic facts — the technique you need is RAG. Here’s a practical guide for Indian businesses. (dgm implements osFoundry, a separate company’s platform — dgm is an independent integration partner, not osFoundry. General information, not professional advice.)
What RAG is
Retrieval-augmented generation combines a language model with a retrieval step that pulls relevant passages from your own documents, so the AI answers from your real knowledge — accurate, current, grounded, with far less hallucination. It’s the foundation for AI that knows your content.
How it works
- Your documents are processed into searchable chunks and embeddings.
- A question retrieves the most relevant chunks.
- The model answers using those chunks plus the question — grounded, with citations.
Quality depends on good document processing, retrieval and grounding — and keeping the index current as documents change.
Data control in India
Your documents may hold personal or confidential data, so they fall under the DPDP Act. The safer approach is a controlled or self-hostable RAG system where documents stay in your environment, with access controls so the AI surfaces only data a user may see (see data residency).
Indian-language documents
Route to Indic-capable models in retrieval and generation so RAG works on Indian-language content — relevant for many Indian businesses.
Common uses
RAG powers document search, internal chatbots, knowledge bases and support deflection.
How dgm helps
dgm builds RAG on osFoundry — grounded in your documents, model-neutral (Indic models for Indian content), self-hostable for India data control — for a $399 assessment and $3,999/month (INR approximate; 18% GST domestic). Model/infrastructure costs are separate and depend on data volume.
General information, not professional advice.