Slide 1: Title Slide Title: ποΈ JanSetu Subtitle: AI for Digital Public Infrastructure & Citizen-Centric Governance Tagline: "Turning scattered citizen voices into ranked, explainable, data-backed public investment decisions." Footer / Badges: Open Digital Public Good | Zero-Cost Architecture | 10+ Indian Languages Slide 2: Problem Statement (The Ground Reality) Header: The Broken Bridge Between Citizens and Governance Key Challenges: π£οΈ Language & Literacy Barrier: Millions of citizens cannot navigate complex English/formal portals or type written text. ποΈ Fragmented Citizen Voice: Grievances arrive scattered across WhatsApp, phone calls, SMS, and physical visits with zero unified view. 𧩠Isolated Ticket Processing: Traditional systems treat every complaint as an isolated ticket rather than detecting systemic ward-level infrastructure crises. π² Subjective Budget Allocation: Decision-makers lack transparent, explainable data on where limited public funds should be deployed first. Slide 3: The Solution β JanSetu Header: Next-Gen AI Layer for Civic Intelligence The 5-Step Continuous Loop: ποΈ Listens: Voice, WhatsApp, or text in 10+ Indian languages (Hindi, Marathi, Gujarati, Tamil, Telugu, Bengali, Kannada, etc.). No app installation needed. π§ Understands: AI extracts location entities (NER), auto-classifies category (Roads, Water, Health, Electricity, etc.), and scores urgency (0β100). π Correlates: Merges citizen demand with local Infrastructure Gap and Budget Allocation data. π Recommends: Policymaker dashboard displays ranked, explainable investment priorities with mathematical breakdown. π Closes the Loop: Automatic tracking IDs (JS-XXXXX) and localized native voice/text status updates sent back to citizens. Slide 4: Citizen Intake & Multilingual AI Layer Header: Zero-Barrier Citizen Experience (Voice-First & App-Less) Core Capabilities: Multilingual Speech-to-Text: Integration with Bhashini API & browser Web Speech API for vernacular voice inputs. Automated Categorization: Rule-based + LLM classifier for 6+ major civic domains (Water Supply, Roads, Electricity, Health, Sanitation, Education). Critical Hazard Detection: Real-time identification of life-threatening emergencies (sparks, open transformers, pipeline bursts, dengue outbreaks). Named Entity Recognition (NER): Pan-India ward, city, and landmark extraction from natural language. Slide 5: ML & Geo-Clustering Intelligence Header: From Scattered Noise to Actionable Clusters Key ML Algorithms: Semantic Text Clustering (TF-IDF + Cosine Similarity): Groups diverse phrasings into single problem clusters (e.g., "paani nahi aa raha" and "water pipeline broken" are recognized as the same issue). DBSCAN Spatial Density Clustering: Geo-tags and clusters complaint coordinates into Hotspot Zones requiring combined municipal intervention. Breeth AI Episodic Memory: Long-term intent memory graph to track recurring citizen issues across time. Slide 6: Transparent & Explainable Priority Engine
Slide 1: Title Slide Title: ποΈ JanSetu Subtitle: AI for Digital Public Infrastructure & Citizen-Centric Governance Tagline: "Turning scattered citizen voices into ranked, explainable, data-backed public investment decisions." Footer / Badges: Open Digital Public Good | Zero-Cost Architecture | 10+ Indian Languages Slide 2: Problem Statement (The Ground Reality) Header: The Broken Bridge Between Citizens and Governance Key Challenges: π£οΈ Language & Literacy Barrier: Millions of citizens cannot navigate complex English/formal portals or type written text. ποΈ Fragmented Citizen Voice: Grievances arrive scattered across WhatsApp, phone calls, SMS, and physical visits with zero unified view. 𧩠Isolated Ticket Processing: Traditional systems treat every complaint as an isolated ticket rather than detecting systemic ward-level infrastructure crises. π² Subjective Budget Allocation: Decision-makers lack transparent, explainable data on where limited public funds should be deployed first. Slide 3: The Solution β JanSetu Header: Next-Gen AI Layer for Civic Intelligence The 5-Step Continuous Loop: ποΈ Listens: Voice, WhatsApp, or text in 10+ Indian languages (Hindi, Marathi, Gujarati, Tamil, Telugu, Bengali, Kannada, etc.). No app installation needed. π§ Understands: AI extracts location entities (NER), auto-classifies category (Roads, Water, Health, Electricity, etc.), and scores urgency (0β100). π Correlates: Merges citizen demand with local Infrastructure Gap and Budget Allocation data. π Recommends: Policymaker dashboard displays ranked, explainable investment priorities with mathematical breakdown. π Closes the Loop: Automatic tracking IDs (JS-XXXXX) and localized native voice/text status updates sent back to citizens. Slide 4: Citizen Intake & Multilingual AI Layer Header: Zero-Barrier Citizen Experience (Voice-First & App-Less) Core Capabilities: Multilingual Speech-to-Text: Integration with Bhashini API & browser Web Speech API for vernacular voice inputs. Automated Categorization: Rule-based + LLM classifier for 6+ major civic domains (Water Supply, Roads, Electricity, Health, Sanitation, Education). Critical Hazard Detection: Real-time identification of life-threatening emergencies (sparks, open transformers, pipeline bursts, dengue outbreaks). Named Entity Recognition (NER): Pan-India ward, city, and landmark extraction from natural language. Slide 5: ML & Geo-Clustering Intelligence Header: From Scattered Noise to Actionable Clusters Key ML Algorithms: Semantic Text Clustering (TF-IDF + Cosine Similarity): Groups diverse phrasings into single problem clusters (e.g., "paani nahi aa raha" and "water pipeline broken" are recognized as the same issue). DBSCAN Spatial Density Clustering: Geo-tags and clusters complaint coordinates into Hotspot Zones requiring combined municipal intervention. Breeth AI Episodic Memory: Long-term intent memory graph to track recurring citizen issues across time. Slide 6: Transparent & Explainable Priority Engine
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This initiative addresses the Civic Voice Gap by leveraging JanSetu, an AI platform designed for citizen-centric governance. It aims to unify multilingual voices across various channels and highlight systemic infrastructure issues. Transitioning from voice to civic intelligence, it listens to and classifies over ten Indian languages, clusters issues by meaning and location, and identifies hazards at the ward level. Furthermore, it promotes explainable investment decisions by ranking...