Custom LLM Development Services

Custom LLM development involves building or fine-tuning a large language model specifically around your business data, workflows, and use cases. Unlike generic tools like ChatGPT, a custom model understands your domain, respects your data privacy requirements, and is optimised to perform within your specific product environment, delivering far more accurate and reliable outputs.

Timelines vary based on complexity and scope. A fine-tuning LLM engagement on an existing foundation model can take 6–12 weeks. A fully custom AI model training project, from data preparation to production deployment, typically spans 3–6 months. During our LLM consulting discovery phase, we’ll give you a precise roadmap and timeline.

Not necessarily. The quality and relevance of your data matter more than raw volume. Through foundation model fine-tuning and RAG (Retrieval Augmented Generation) architectures, we can build highly effective domain-specific AI models even with moderate datasets, as long as the data is well-structured and representative of your use case.

Data security is central to our process. We offer private LLM deployment options where all training and inference happen within your own cloud environment or on-premise infrastructure. We also follow strict data handling protocols, sign NDAs, and implement access controls at every stage , so your sensitive information never leaves your control.

RAG (Retrieval Augmented Generation) is an architecture that connects your LLM to a live knowledge base, allowing it to fetch and reference up-to-date information before generating a response. If your use case requires factual accuracy, real-time knowledge, or traceability of answers, RAG is highly recommended and often essential for enterprise-grade deployments.

Absolutely. Our LLM integration services are designed to embed your custom model into your existing tech stack, whether through REST APIs, SDKs, or direct integration into your CRM, ERP, support platform, or internal tools. We ensure the integration is scalable, well-documented, and doesn’t disrupt your current workflows.

We use a multi-dimensional evaluation framework that tracks hallucination rates, response accuracy, task completion rates, latency, and user satisfaction scores. Before any deployment, we run red-teaming exercises and structured testing protocols, and post-launch, we set up continuous monitoring dashboards so performance is always visible and improvable.

We have delivered LLM application development and domain-specific AI models across healthcare, BFSI, legal, e-commerce, education, and software & technology sectors. Our deep domain expertise means we don’t just build technically sound models, we build models that understand the nuance, compliance requirements, and data characteristics specific to your industry.