LLM Development Services in India

A large language model, or LLM, is an AI model trained on vast amounts of text. It can understand and generate language, answer questions, summarise, translate and write code.

RAG stands for retrieval-augmented generation. The system first finds relevant passages in your documents, then gives them to the model to answer. It keeps answers current, specific and traceable, without retraining the model.

Fine-tune when you need a consistent style, format or specialised vocabulary that prompts and retrieval cannot deliver. For most knowledge tasks, RAG gives better value and is easier to update.

Hosted models are quicker to start and often more capable. Running open-weight models yourself gives more control over data and cost at scale. We help you compare on accuracy, privacy and total cost.

LLM projects are priced by how much content there is and how messy it is, how many systems and users are involved, the accuracy required and where the model is hosted. A retrieval assistant over a clean document set is a modest project, while a private, fine-tuned deployment with strict access rules is larger. A discovery sprint from $1,000 tests the idea on your own documents and ends with a scoped quote.

A working prototype usually takes 3 to 5 weeks. A production system with evaluation, security and integrations takes 8 to 16 weeks.

We ground answers in your sources, test against curated questions, measure scores on every change and add fallbacks for uncertain answers.

We can use private endpoints, run models in your own cloud account or on-premise, mask sensitive fields and keep full audit logs.

Yes. We evaluate models for Hindi, Telugu, Tamil and other languages, and choose or tune the one that performs best for your content.

Yes. You can add LLM and AI engineers to your team monthly, with a typical ramp-up of 10 to 15 business days.

We build an evaluation set from real questions and the answers your experts expect, run it on every change, and score accuracy, grounding in sources, safety and response time. Failures are reviewed by people, and the set grows as new cases appear.

Choose RAG when answers must come from documents that change, need a visible source or depend on who is asking. Fine-tuning suits a fixed style, format or narrow skill that prompts alone cannot teach. Many projects use RAG first, because it is faster to build, easier to update and easier to check, and add fine-tuning only if testing shows a gap.

Most business content works: PDFs, Word and slide files, wikis, ticketing and CRM records, email archives, databases and websites. We connect each source, split and index it, keep the index in step with changes, and carry over the permissions of the original system.

Yes. We carry each user's access rights into retrieval, so the assistant only searches documents that person may see. Sensitive fields can also be masked before text reaches a model.