LLM Fine-tuning
Custom models trained on your data with LoRA and QLoRA — for domain language, consistent behaviour and materially lower inference cost.
When you are running high volume, need a specific tone or format every time, or work in language a general model handles poorly, fine-tuning earns its keep. We fine-tune open models on your proprietary data, evaluate honestly against the base model, and deploy where you need it — including entirely inside your own cloud.
What this solves
- Frontier model API costs scaling painfully with volume
- Models missing industry jargon or regional language
- Inconsistent tone and formatting across responses
- Data that is not allowed to leave your infrastructure
What you get
- Dataset preparation and curation from your data
- LoRA or QLoRA fine-tuning on an appropriate open model
- Evaluation against the base model on your own test set
- Deployment in your cloud or ours
- A retraining path as your data grows
Technology
Industries
Frequently asked
Do you offer LLM fine-tuning services in India?
Yes. We fine-tune Llama, Mistral and Gemma on proprietary client data using LoRA and QLoRA, and deploy the result either in our infrastructure or entirely within your own AWS, Azure or GCP environment. Typical motivations are reducing hallucination on domain material, cutting per-token inference cost at volume, and handling Indian-language content that general models handle poorly.
Talk to the engineers who build it
Tell us the problem and we'll tell you whether llm fine-tuning is the right answer — and what it would take.