Northeast India

AI Infrastructure & MLOps across Assam

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

Districts
23
PIN codes
571
Cities mapped
14

AI Infrastructure & MLOps in Assam

On-premise inference makes sense more often than the cloud narrative suggests, at steady high volume, or where data simply cannot leave. We model both honestly.

Assam runs on tea, petroleum and natural gas, agriculture and handloom and silk, plantation and energy operations spread across difficult terrain, which makes remote monitoring and field-data capture the recurring need. Where ai infrastructure & mlops earns its budget here usually follows directly from that mix.

We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep. We hand over with runbooks, tests and a team that knows how it works, not a dependency.

নমস্কাৰ , Nomoskar. We work in Assamese and English across Assam.

Assam coverage

State / UT
Assam
Region
Northeast India
Districts covered
23
PIN codes covered
571
Cities mapped
14
Working languages
Assamese, 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 Assam?

Yes, all 23 districts and 571 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Assam sectors do you work with most?

Across Assam the economy leans towards tea, petroleum and natural gas, agriculture, handloom and silk. Plantation and energy operations spread across difficult terrain, which makes remote monitoring and field-data capture the recurring need.

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 Assam

Covering all 23 districts. Tell us what you are trying to change.

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