North India

AI Infrastructure & MLOps across Rajasthan

GPU infrastructure, model serving and MLOps pipelines, sized for your workload, not for a benchmark. Covering every district and PIN code in Rajasthan.

Districts
32
PIN codes
992
Cities mapped
29

AI Infrastructure & MLOps in Rajasthan

Orqent Labs builds AI infrastructure sized to the workload you actually have, with the rollback paths you will eventually need.

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 ai infrastructure & mlops 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. You own the code, the models where they are open-weight, and the documentation to run it without us.

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

  • Workload sizing based on measured throughput, not guesses
  • Model registry and versioned deployments
  • Autoscaling and cost-per-inference monitoring
  • Canary and rollback deployment paths
  • On-premise or air-gapped options where required
  • Runbooks and on-call documentation

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.

Cloud or on-premise?

We model both against your real volume. On-premise typically wins at sustained high throughput or where data residency is non-negotiable; cloud wins on variable and early-stage workloads.

Can you deploy air-gapped?

Yes, with open-weight models and a fully offline inference stack, the usual pattern for defence, and for some healthcare and government work.

Do you support our existing Kubernetes setup?

Yes, and we would rather extend it than introduce a parallel platform your team has to learn.

AI Infrastructure & MLOps in Rajasthan

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

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