North India

Custom Model Fine-tuning across Rajasthan

Fine-tuned and distilled models for your domain, cheaper, faster and more consistent than prompting alone. Covering every district and PIN code in Rajasthan.

Districts
32
PIN codes
992
Cities mapped
29

Custom Model Fine-tuning in Rajasthan

Orqent Labs fine-tunes and distils models for teams with genuine volume, where the economics of inference have started to matter more than the ceiling of capability.

Rajasthan runs on mining and minerals, tourism, textiles, cement, handicrafts and solar energy, mining and utility-scale solar, where asset monitoring and field-data capture are the recurring problems. Where custom model fine-tuning earns its budget here usually follows directly from that mix.

Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners. Six weeks to something running in production, not six quarters to a strategy document.

नमस्ते , Namaste. We work in Hindi and English across Rajasthan.

Rajasthan coverage

State / UT
Rajasthan
Region
North India
Districts covered
32
PIN codes covered
992
Cities mapped
29
Working languages
Hindi, English

What is included

  • Honest assessment of whether fine-tuning is warranted
  • Training data curation and quality review
  • LoRA or full fine-tune as the workload justifies
  • Evaluation against the prompted baseline
  • Inference deployment and cost comparison
  • Retraining pipeline as your data grows

Questions

Do you cover all of Rajasthan?

Yes, all 32 districts and 992 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Rajasthan sectors do you work with most?

Across Rajasthan the economy leans towards mining and minerals, tourism, textiles, cement, handicrafts, solar energy. Mining and utility-scale solar, where asset monitoring and field-data capture are the recurring problems.

Should we fine-tune?

Usually not first. Prompting and retrieval solve most problems more cheaply. Fine-tuning wins for consistent format, narrow domain style, and high-volume tasks where a smaller model can replace a larger one.

How much data do we need?

For LoRA on a narrow task, often a few thousand high-quality examples. Quality matters far more than volume. We review the dataset before training anything.

Can we own the model?

With open-weight base models, yes. You hold the weights and can run them on your own infrastructure indefinitely.

Custom Model Fine-tuning in Rajasthan

Covering all 32 districts. Tell us what you are trying to change.

Or email bd@dtrasglobal.com · call +91 74118 77878