How Indian IT Consultancies Are Structuring Custom LLM Offerings

Enterprises want language models that understand their own documents, terminology and processes, not just general knowledge. Indian IT consultancies are responding with structured service offerings for custom large language model solutions. This article explains the main types of customisation, how engagements are typically packaged, and what clients should consider.

Levels of Customisation

“Custom LLM” can mean very different things. Consultancies usually describe a ladder of options, from light to heavy:

• Prompt engineering and templates: carefully designed instructions that steer an existing model for specific tasks.
• Retrieval-augmented generation: connecting a model to company documents so it answers using current, verified information.
• Fine-tuning: training an existing model further on domain examples to improve style, terminology or task accuracy.
• Small specialised models: deploying compact open models tuned for a narrow job, often cheaper to run at scale.
• Training from scratch: rarely justified, reserved for organisations with unique data and substantial resources.

Most enterprise needs are met by the first three options. Retrieval-augmented generation is especially popular because it keeps knowledge up to date without retraining and allows answers to cite their sources.

How Engagements Are Packaged

Consultancies tend to structure work in phases. An initial discovery phase identifies high-value use cases, assesses data readiness and defines success measures. A proof of concept then tests one use case with real users, often within a few weeks.

If results are promising, the engagement moves to production, which involves security reviews, integration with existing systems, monitoring and user training. Finally, a managed services phase covers ongoing model updates, evaluation, cost control and support.

Commercial models vary. Some engagements are priced by time and materials, others by fixed-scope deliverables, and a growing number include outcome-linked components tied to measures such as documents processed or support tickets resolved.

What Clients Should Consider

Data governance comes first. Clients should understand where their data will be processed, whether it will be used to train shared models, and how access is controlled. Options range from cloud-hosted models with contractual protections to private deployments inside the client’s own environment.

Evaluation should be agreed upfront. A clear test set of representative questions, with expected answers reviewed by domain experts, makes it possible to compare approaches objectively and catch regressions later.

Running costs deserve scrutiny. Large models can become expensive at high volume, so consultancies often recommend routing simple requests to smaller models and reserving larger ones for complex tasks.

Finally, clients should avoid excessive lock-in by asking for documentation, portable components and a clear handover plan.

The Essentials

Indian IT consultancies are turning custom LLM work into phased, repeatable offerings, ranging from prompt design and retrieval systems to fine-tuned specialised models. Clients get the most value by matching customisation depth to genuine need, insisting on strong data governance and agreeing on clear evaluation and cost controls from the start.