MP CivicConnect turns scattered citizen reports into verified, prioritized civic action.
Turn Citizen Voices Into Civic Action.
Live 3D civic map · Bhopal
Tower height = priority · drag to orbit · click a tower to open the issue
Building the 3D civic city…
Civic issues
16
clustered from citizen reports
Reports merged
28
12 duplicates prevented
Citizens engaged
1096
supporters across issues
Verified resolved
13%
AI + community confirmed
Report
Text, photo, location
01AI Understands
Category, severity, summary
02Community Validates
Support, evidence, discussion
03Authority Acts
Priority queue, status, response
04Resolution Verified
AI + citizen confirmation
05The problem
Civic complaints today are scattered across phone calls, WhatsApp groups, social posts and separate portal tickets. The same problem is filed a hundred times and counted as a hundred unrelated issues — so nothing is comparable, nothing is verifiable, and nothing can be prioritised fairly.
One pothole reported by forty riders reads as forty small problems instead of one significant one.
Photos live in private chats. There is no single record an officer can act on or a citizen can audit.
Attention follows whoever shouts loudest online, not the water contamination affecting a whole sector.
How it works
Every stage is visible to both citizens and authorities. Nothing is a black box.
Text, photo, optional video, and a precise location. Two minutes on a phone, in the street.
Category, sub-type, severity, urgency, safety risk, a neutral one-line summary, and the likely owning authority.
Location proximity plus semantic similarity merges repeat reports into a single record with a real reporter count.
Neighbours declare they are affected, add evidence, and discuss local impact in one thread.
An explainable score orders attention. Community support is capped at 10 of 100 points by design.
Status updates and an official response, then before/after evidence checked by AI and confirmed by citizens.
AI-powered civic intelligence
Every AI output is structured, labelled and explainable. The model classifies, summarises and assesses; deterministic code decides identity and priority — so the same inputs always produce the same civic record.
Understanding
Category, sub-type, severity, urgency, safety risk, summary and likely authority, as structured JSON.
Duplicate clustering
Deterministic proximity + text similarity + category match. Auditable, replayable, never a guess about database rows.
Community summarisation
What citizens are actually saying, distilled into the shared concerns an officer needs to read.
Resolution verification
Before/after evidence assessed with a confidence score — a signal for citizens, never the final verdict.
The prioritisation principle
AI prioritizes the order of attention, not whether an issue deserves to exist.
A high number of upvotes does not make an issue more important. Severity, urgency, affected citizens and evidence strength do. Every valid issue stays in the system and can eventually be addressed — priority only orders attention.
Live from the Bhopal pilot
Pothole reported at Zone-II main road, near the bus stop that may create a safety risk for people using the location.
Water contamination reported at Sarvadharm C-sector distribution line that may create a safety risk for people using the location.
Community participation
Localities get their own space to document problems together — posts, polls and shared evidence, all anchored to real civic issues rather than opinions.
MP Nagar, Arera Colony, Kolar, New Market and Shahpura each track their own active and resolved issues.
Comments feed the AI summary an officer reads, so local knowledge reaches the record.
Campaigns build documented support around one verified issue — collective civic participation, with evidence.
Government dashboard
A separate, desktop-optimised workspace for municipal teams: a priority queue instead of an inbox, one brief per civic issue, and a resolution flow that requires evidence.
Simulated authority environment. No real government body is represented, contacted or committed to any action.
Active
14
Critical
1
High priority
7
In progress
3
Pending verify
1
Resolved
2
Median priority across open issues: 67/100 · Average open age: 15 days
Impact
This is the whole product in one column.
100 separate reports
The same pothole reported a hundred times, in a hundred places, in a hundred formats. Nothing adds up.
1 verified civic issue
Reports are classified, summarised and clustered by location and meaning into a single civic record.
327 supporters
Citizens confirm they are affected, add evidence, and describe local impact in one shared thread.
Priority 89/100
An explainable score orders attention by severity, urgency, affected citizens and evidence — not by popularity.
In Progress
The authority dashboard receives one actionable brief instead of a hundred fragmented complaints.
Resolution Verified
AI reviews the before/after evidence, and citizens on the ground confirm or reopen the issue.
Illustrative walk-through of the product loop using demo / simulated civic data.
The pilot covers MP Nagar, New Market, Arera Colony, Kolar, Habibganj, Shahpura, Govindpura and Jawahar Chowk. Small enough for real duplicate clustering to matter, large enough to cover roads, water, drainage, waste, lighting, traffic and metro construction.
Localities, categories and authorities are data, not code. Adding Indore, Jabalpur, Gwalior or Ujjain is a configuration change plus a ward map — the AI layer, clustering rules and priority model stay identical, so comparisons across cities remain fair.
Claude turns those signals into actionable intelligence. Authorities can act. Citizens can verify the outcome.