AI-Enabled Public Grievance Redressal: A Framework for Citizen-Centric Responsiveness in Indian States

Grievance redressal is where citizens test whether government listens. Indian governments receive millions of complaints each year through portals, call centres, social media, letters and in-person visits. The volume, the variety of languages and the number of departments involved make it hard to route complaints correctly, resolve them on time and learn from patterns. Artificial intelligence (AI), and in particular natural language processing for Indian languages, can help with each of these steps — but only if it is introduced with clear safeguards and with officers, not algorithms, remaining accountable for decisions. This paper proposes a five-layer framework for AI-enabled grievance redressal in Indian states: (1) multilingual, multichannel intake; (2) classification, routing and prioritisation; (3) officer assistance for resolution; (4) closure verification and citizen feedback; and (5) analytics for root-cause and policy learning. For each layer it sets out what AI can do, the human checks required and suitable performance measures. It also discusses governance, data protection and change-management requirements, and suggests a phased path from pilot to state-wide adoption. The paper is a design framework drawn from e-governance practice and the published literature; it does not report results from a deployed system.