SarkarSathi
AI co-pilot for municipal accountability in India · Jan – Mar 2026
Tags: AI, Civic Tech, Python, FastAPI, Gemini AI, Vector DB
India Innovates 2026 · Top 1,000 / 26,000+ entries · National media coverage
The problem
Civic complaints in India vanish into systems that were never designed to respond. Citizens know what's broken in their ward — potholes, water supply failures, illegal construction — but translating lived experience into something a government portal will actually process is a skill most people don't have, and shouldn't need. The problem isn't awareness. It's structured escalation.
What it does
SarkarSathi started as a complaint co-pilot: the AI helps citizens frame their issue clearly, identifies the right authority, and routes the complaint with the language and documentation that actually gets responses. For India Innovates 2026, it evolved into full governance intelligence for elected representatives — commitment tracking across campaign promises, complaint clustering by district and severity, and an agentic advisor that surfaces what needs attention before it becomes a crisis. Built as the sole technical member on a 5-person remote team. During board exams.
How it works
- Complaint intake: Conversational intake via chat. OCR for photo evidence. Structured output that matches government portal formats.
- Semantic clustering: FastAPI + vector database groups similar complaints across districts. A hundred separate "no water" complaints surface as one data point.
- Governance intelligence: Representatives see a real-time dashboard: commitments made vs. kept, complaint surge detection, agentic recommendations on where to direct resources.
- Agentic advisor: Gemini AI reads the complaint corpus and generates specific, actionable briefings — not summaries, but decisions that can be acted on immediately.
Impact
- Top 1,000 from 26,000+ India Innovates entries
- 6 GitHub stars · 3 forks
- 5-person team · sole technical lead
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