Government & Public Sector
AI Infrastructure & MLOps for Government & Public Sector
AI Infrastructure & MLOps for government & public sector, built around the constraint that defines the sector: procurement, data sovereignty and accessibility obligations shape the architecture before anything else.
- Regulations in scope
- 5
- Systems we integrate
- 4
- Typical first release
- 6 weeks
What changes when it is government & public sector
A model registry with versioned deployments is what lets you roll back in minutes. Without it, a bad model version becomes a very long evening.
In government & public sector, procurement, data sovereignty and accessibility obligations shape the architecture before anything else. That single fact reshapes how ai infrastructure & mlops has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.
The workload we are most often asked to take on first is multilingual service delivery, usually integrated against grievance platforms. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.
Built by engineers who ship production systems, not by a practice that subcontracts the build. Six weeks to something running in production, not six quarters to a strategy document.
The sector constraints we design around
- Defining constraint
- procurement, data sovereignty and accessibility obligations shape the architecture before anything else
- Regulations in scope
- DPDP Act 2023 · RTI obligations · GIGW accessibility guidelines · government cloud empanelment · e-governance standards
- Systems of record
- departmental portals · DigiLocker and Aadhaar-linked services · legacy record systems · grievance platforms
- Where we usually start
- citizen grievance triage
AI Infrastructure & MLOps workloads in government & public sector
- citizen grievance triage
- scheme eligibility checking
- records digitisation
- multilingual service delivery
- case file processing
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 from this sector
Can AI systems be procured under GeM?
Yes, and we structure deliverables to fit standard procurement categories and evaluation criteria.
Does it work in regional languages?
It has to. Public services in India are multilingual by obligation, and we build for that rather than adding translation later.
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.
Other capabilities for government & public sector
- AI Agent Development for Government & Public Sector
- Agentic Workflow Automation for Government & Public Sector
- LLM Application Development for Government & Public Sector
- RAG & Knowledge Retrieval for Government & Public Sector
- Chatbot Development for Government & Public Sector
- Document Processing & IDP for Government & Public Sector
- AI Copilot Development for Government & Public Sector
- Data Engineering for Government & Public Sector
- Enterprise AI Platform for Government & Public Sector
- MCP Server Development for Government & Public Sector
AI Infrastructure & MLOps for government & public sector, worth a conversation?
Tell us the workload and the regulation it sits under. We will tell you what is realistic.
Or email bd@dtrasglobal.com · call +91 74118 77878