Buyer's guide · Updated November 2026
Custom LLM development companies in India: how to choose.
India now has five very different kinds of company selling custom large language model work. Picking the wrong kind costs months. This guide explains each type, who it suits, and the questions to ask before you sign.
The landscape
Five kinds of LLM partner.
Not every company that says custom LLM means the same thing. Some build their own foundation models, some integrate models inside large transformation programmes, and some fine-tune and deploy existing models for a specific workflow.
| Type | Examples | Best for | Watch for |
|---|---|---|---|
| Foundation-model builders | Sarvam AI, CoRover (BharatGPT) | Indian-language models, speech and translation APIs, sovereign AI | You still need a team to turn an API into a working business system |
| Large IT services firms | TCS, Infosys (Topaz), Wipro, HCLTech, Tech Mahindra | Multi-year, multi-country transformation with existing vendor contracts | Minimum deal sizes and long procurement cycles |
| AI and analytics firms | Fractal, Tiger Analytics, Quantiphi | Data-science-heavy programmes in large enterprises | Engagements are usually sized for very large companies |
| Conversational AI platforms | Yellow.ai, Haptik, Gnani.ai, Uniphore | Customer-service chat and voice at scale on a vendor platform | Platform licence and lock-in; less suited to back-office or document work |
| Specialist AI studios | MNB Research and other focused studios | Mid-size and enterprise teams that want one accountable team from scoping to support | Check that the studio runs real systems in production, not just demos |
Descriptions of other companies are summarised from their own public websites and announcements, checked November 2026. Offerings change; confirm current scope with each vendor before you shortlist.
How to evaluate
Six questions to ask any LLM vendor.
Will you adapt or build?
A credible partner says plainly whether it fine-tunes existing models or trains from scratch, and why.
Where will it run?
Your cloud in an Indian region, your premises, or the vendor's tenancy. Get it in writing.
How will you test it?
Ask for an evaluation set agreed with your team before launch, with pass marks.
Who owns the weights and data?
Fine-tuned weights, prompts and logs should belong to you.
What happens when a model changes?
Providers retire models. Ask how the vendor reroutes and re-tests without downtime.
Show me production
Ask to see a system the vendor itself runs, with real users, not a slide.
Where MNB Research fits
- Fine-tuning and retrieval on existing models, never claimed foundation-model training
- Deployment on your cloud or premises when data must stay in India
- Evaluation sets agreed with your team before launch
- Routing and governance through MNB Intelligence
- 13 of our own AI products run in production on the same stack
When to choose someone else
- You need a new foundation model for an Indian language: look at foundation-model builders
- You need a global system integrator across many countries: look at the large IT services firms
- You only need a customer-service chatbot on a ready platform: look at conversational AI platforms
Scope a custom model.
A 30-minute call with the founder, then a written proposal: what the model will do, the data it needs, how it will be tested and what it will cost.
Questions
Frequently asked.
Which company is best for custom LLM development in India?
It depends on the job. Foundation-model builders such as Sarvam AI suit Indian-language models and APIs; large IT services firms such as TCS and Infosys suit multi-year, multi-country programmes; analytics firms such as Fractal suit decision-science-heavy work; specialist studios such as MNB Research suit mid-size and enterprise teams that want one accountable team to scope, fine-tune, deploy and support a model on their own infrastructure.
Do I need to train a model from scratch?
Almost never. Most business problems are solved by retrieval over your documents, by fine-tuning an existing open-weight or commercial model, or by both. Training from scratch costs far more and is only justified for new languages, modalities or very large proprietary corpora.
Can a custom LLM run on our own servers in India?
Yes. Open-weight models can be deployed on your cloud account in an Indian region or on your own hardware, so prompts and documents never leave your control. This matters for DPDP Act obligations and sector rules in banking, insurance and healthcare.
How long does a custom LLM project take?
A scoped pilot on one workflow usually takes four to eight weeks: data review, a baseline, fine-tuning or retrieval, an evaluation set your team agrees with, then a controlled rollout.
What does MNB Research do differently?
MNB adapts proven models rather than claiming to build foundation models, tests them against an evaluation set agreed with your team before launch, deploys on your infrastructure if you need it, and runs 13 of its own AI products in production, so the same engineering is used every day.