The PIB Backgrounder outlines India’s AI-powered transformation of its statistical and data ecosystem, detailing platforms, tools, and institutional efforts to embed AI across public data systems and governance.

AI/ML tools for classification and discovery include a NIC code classification tool and the MoSPI StatsDoc AI Assistant, enabling natural language queries to streamline NIC coding and document search while expanding access to official data.

AI use cases span sectors, with trusted AI in healthcare through BODH, ABDM, and SAHI, plus AI-driven meteorology, and initiatives like Bharat-VISTAAR that aim to support farming via multilingual advisory systems and pest surveillance.

Aadhaar innovations feature AI-based biometric deduplication and advanced document verification, with Aadhaar Vision 2032 planning to integrate AI, blockchain, quantum tech, and stronger security to bolster resilience.

Institutional architecture and capacity building are advancing AI adoption through the Data Innovation Lab under DIID, MoSPI MoUs, expanded AI use cases, and training programs such as NSSTA and national capacity-building language initiatives.

BharatGen, a sovereign multilingual LLM launched in 2025, supports 22 languages and multimodal capabilities, reinforcing a domestically developed AI stack for public use.

The document cites PIB pages, MOSPI portals, and partner institutes as sources for further detail.

NDAP 2.0 emphasizes cross-sector analytics, data discoverability, and AI/ML-based search with pre-curated visual insights and domain modules.

An AI-based legacy data extraction tool converts legacy NSO data from PDFs, images, and spreadsheets into a usable database for analysis.

The report frames AI as an enabling layer across India’s public data landscape to deliver inclusive, accessible, and policy-relevant data while navigating linguistic diversity and development gaps.

AI-enabled data access is enhanced by the e-Sankhyiki portal with Model Context Protocol and semantic search, plus an AI-powered chatbot on MoSPI’s site for natural language queries.