North India
AI Infrastructure & MLOps across Haryana
GPU infrastructure, model serving and MLOps pipelines, sized for your workload, not for a benchmark. Covering every district and PIN code in Haryana.
- Districts
- 19
- PIN codes
- 314
- Cities mapped
- 19
AI Infrastructure & MLOps in Haryana
Orqent Labs builds AI infrastructure sized to the workload you actually have, with the rollback paths you will eventually need.
Haryana runs on automotive, IT and business services, agriculture, textiles and engineering goods, the Gurugram corporate belt alongside a working auto-manufacturing cluster, which puts back-office and shop-floor automation in the same state. Where ai infrastructure & mlops earns its budget here usually follows directly from that mix.
Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move. 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 Haryana.
Haryana coverage
- State / UT
- Haryana
- Region
- North India
- Districts covered
- 19
- PIN codes covered
- 314
- Cities mapped
- 19
- 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
Districts of Haryana
Every district has a coverage page listing its PIN codes.
Other capabilities across Haryana
Questions
Do you cover all of Haryana?
Yes, all 19 districts and 314 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.
Which Haryana sectors do you work with most?
Across Haryana the economy leans towards automotive, IT and business services, agriculture, textiles, engineering goods. The Gurugram corporate belt alongside a working auto-manufacturing cluster, which puts back-office and shop-floor automation in the same state.
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 Haryana
Covering all 19 districts. Tell us what you are trying to change.
Or email bd@dtrasglobal.com · call +91 74118 77878