Retrieval-augmented generation (RAG) is the foundation of accurate internal AI — grounding answers in your own data. For Indian companies, the build approach and data residency shape the choice. Here’s a grounded comparison. (dgm implements osFoundry, a separate company’s platform — dgm is an independent integration partner, not osFoundry.)
What RAG is, briefly
RAG connects an AI model to your data — it retrieves relevant company information and uses it to ground answers, so the AI responds from your knowledge rather than guessing. It’s what makes internal chatbots and enterprise search accurate.
The options
| Approach | India residency | Best for |
|---|---|---|
| Framework (LangChain) | Self-deploy anywhere | Engineering teams wanting full control |
| Open-source app platform (Dify) | Self-host → strong | Free self-hosted RAG builder |
| Managed neutral platform (osFoundry) | Self-host in India | Speed with less to operate |
See osFoundry vs LangChain and osFoundry vs Dify for the detail. For most Indian businesses, a platform is faster than building RAG from scratch; build only where RAG is a core differentiator (see build vs buy).
The India residency point
Your indexed company data is sensitive, so residency matters under the DPDP Act. Self-hostable RAG platforms (Dify, self-hosted osFoundry) deployed on Indian infrastructure keep the indexed data and inference in-country — the cleanest answer. Managed SaaS RAG depends on the vendor’s India residency, which varies.
What actually determines RAG quality
A common mistake is obsessing over the model or the vector database. RAG quality is mostly won in the engineering around the model:
- Data preparation — clean, governed source data.
- Chunking — how documents are split for retrieval.
- Retrieval quality — surfacing the right context.
- Grounding + citations — answering from retrieved data, with sources.
A powerful model on poorly prepared data still gives confidently wrong answers. This is where an experienced implementer earns their keep.
Where osFoundry fits
osFoundry provides RAG plumbing — knowledge bases, retrieval, model-neutral routing (including Indian-language models) and grounding — self-hosted in India for residency. It’s younger with limited independent coverage, so dgm validates the build.
How dgm helps
dgm builds RAG on osFoundry — preparing and chunking your data, setting up retrieval and grounding with citations, routing to the right models, and self-hosting in India where required. Transparent pricing: $399 assessment, $3,999/month implementation, no per-seat fees (INR approximate; 18% GST for domestic clients). Explore the platform at osFoundry, or talk to dgm about a RAG build.
General information, not legal advice. Confirm DPDP obligations with counsel before indexing sensitive data.