Sector
Population-scale AI on top of population-scale digital public infrastructure — for the citizen, the regulator, and the auditor.
Use Cases
- Citizen-service chatbots in 22 official languages over scheme, eligibility, and grievance corpora — Bhashini-backed.
- Document digitisation and grievance routing — OCR plus LLM classification, integrated with CPGRAMS-style workflows.
- Fraud and leakage detection in welfare disbursement (PDS, DBT) and tax flows (GST input-tax-credit chains).
India Stack to Citizen
India is the only country building population-scale AI on top of population-scale digital public infrastructure. The firms that ship inside that stack will define the decade.
Where we work
Central and state government departments, state-level digital governance bodies, public-sector undertakings, regulatory authorities, and quasi-government platforms that sit on top of India Stack (Aadhaar, UPI, DigiLocker, ABDM, Bhashini, ONDC). Programme-mode work, not vendor-tender churn.
Where AI changes the economics
- Citizen services in 22 languages. Bhashini-grounded multilingual chat and voice for scheme discovery, eligibility checks, grievance filing, and status tracking.
- Document digitisation at scale. Decades of paper records, certificates, court filings, and land records — converted, classified, and retrieval-ready under GIGW-grade access control.
- Fraud and leakage detection. Pattern detection in PDS, DBT, MGNREGA, and GST flows that flags anomalies for human investigators with full evidentiary trail.
- Regulator and policy-analyst copilots. Internal knowledge surfaces over circulars, judgements, parliamentary questions, and committee reports. Faster briefs, better citations.
- Field-officer support. Tablet-grade copilots for ANMs, ASHA workers, agricultural extension officers, and revenue officers — operating inside intermittent-connectivity and shared-device realities.
How public-sector AI has to be built
Audit-traceable from prompt to decision. Data-resident inside the relevant ministry’s perimeter. Empanelled, STQC-tested, and accessible under GIGW. CAG-auditable when the AI influences a benefit determination. And designed for failure modes — the model must degrade gracefully when the citizen sitting in front of a CSC operator cannot wait for an LLM that is rate-limited today.
What we will not do
Train on citizen data without DPDPA-clean consent and explicit purpose specification. Influence benefit determination without auditable human-in-loop. Lock a programme into a vendor stack that the next government cannot inspect or replace.
What you get
Sovereign-grade AI deployed inside ministry, state, or PSU infrastructure. Built to GIGW and STQC standards, audit-traceable by design, and aligned with the IndiaAI Mission programme architecture. The citizen interaction improves; the audit trail proves it.
Regulatory
- MeitY IndiaAI Mission guardrails plus DPDPA 2023 consent and data-localisation for citizen data.
- GIGW and STQC accessibility and security empanelment, plus CAG-auditable model decisioning for any benefit determination.
Relevant Services
Related Insights
- DPDP Act and Generative AI: What Indian Enterprises Must Implement
- Building an AI Audit Trail Regulators Will Actually Accept