LLM Fine-Tuning & Custom Model Development
Your data, your domain, your model — tuned for enterprise-grade accuracy.
Off-the-shelf models know the internet; they don't know your domain. We fine-tune large language models on your data — instruction tuning, LoRA/QLoRA parameter-efficient training, and domain adaptation of open-weight models like Llama, Mistral and Gemma — so the model speaks your terminology, follows your formats and stays inside your compliance boundaries.
Fine-tuning is an engineering discipline, not a button: we start with an honest RAG-vs-fine-tuning assessment (often the answer is both), build clean training datasets from your real interactions, train with rigorous held-out evaluation, and ship the tuned model behind a production endpoint — on your cloud, on-premise, or serverless — with monitoring and versioning.
₹6,50,000
one-time · 6–10 weeks
₹40,000/mo
hosting, monitoring & tuning
Why teams choose this
Domain accuracy generic models can't match
Tuned on your vocabulary, formats and edge cases, the model stops sounding generic and starts sounding expert.
Smaller, faster, cheaper
A fine-tuned small model often beats a giant general model on your task — at a fraction of the latency and cost.
Your data stays yours
Training runs in your cloud or ours under strict controls; the resulting model weights belong to you.
Measured, not assumed
Every run ships with an evaluation suite that scores the tuned model against the base model on your real tasks.
Our proven process
Feasibility & strategy
We assess whether fine-tuning, RAG or both fits your goal, and define measurable success criteria.
Data curation
We build and clean the training set from your documents, transcripts and interactions — quality over volume.
Train & evaluate
Parameter-efficient training with held-out evals against the base model on your actual tasks.
Deploy & monitor
We serve the tuned model on your infrastructure with versioning, drift monitoring and rollback.
Modern, best-in-class stack
LLM Fine-Tuning services near you
Common questions
They solve different problems. RAG injects fresh knowledge at question time; fine-tuning changes how the model behaves — tone, format, domain reasoning, terminology. Knowledge that changes weekly belongs in RAG; skills and style belong in fine-tuning. Many production systems we build use both, and we assess this honestly before any training run.
Less than most teams expect. With parameter-efficient methods like LoRA, a well-curated set of 500–5,000 high-quality examples often outperforms tens of thousands of noisy ones. We help you extract and clean that dataset from support tickets, documents and past interactions.
Open-weight models such as Llama, Mistral, Gemma and Qwen — which you can own and host anywhere — plus provider fine-tuning APIs where they fit. We recommend the smallest model that meets your quality bar, because it's cheaper and faster in production.
Yes. Training runs inside your cloud account or an isolated environment with encryption and access controls, datasets are never reused across clients, and the tuned weights are delivered to you. For regulated industries we support fully on-premise pipelines.
Explore what pairs well
Let's build what's next.
A free 30-minute consult. We'll map your highest-ROI AI use case and show you exactly how we'd ship it.