Reactive by default
Issues are usually found when residents complain — not when they start.
Urban intelligence platform
City signals. Clear decisions. Visible action.
IntelCit helps municipal operations teams turn street images, video, and location data into explainable evidence, clear priorities, and defensible action — before issues become public complaints.
Built for public works, municipal operations, and city service teams.
Cities still manage streets by complaint
Street cleanliness and public-space upkeep are usually managed reactively, with evidence that is hard to compare and priorities that are hard to defend.
Inconsistent evidence
Manual inspections vary by person, route, and day, so results are difficult to compare or defend.
Hard to prioritize
Without measurable street conditions, crews and budgets are assigned by guesswork.
How IntelCit works
One repeatable path from street observation to municipal action.
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Observe
Capture street conditions from uploaded photos, fixed cameras, and field video with GPS.
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Understand
AI analysis turns each capture into explainable counts, scores, and condition levels, with confidence shown where the service supports it.
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Prioritize
Consistent scoring shows which locations need attention first, across routes and over time.
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Act
Teams receive recommended actions, history, and reports to direct work and show results.
Services available today
Three services are registered on the platform, each labeled with its real delivery stage.
A working view for daily operations
Dashboards bring scores, priorities, and activity together so teams can see status and direct work from one place.
Operations overview
Sample data
128
Street segments reviewed
3.2 / 4
Average cleanliness score
17
Open cleanup actions
- Riverside Park entrance — litter cleared Clean
- Elm Street, block 400 — litter accumulating Attention
- Transit stop 12 — cleanup recommended Urgent
Illustrative preview built with sample data — not live operational metrics.
Evidence you can explain
Every result keeps its source. IntelCit reports observations with confidence levels instead of hiding decisions behind a black box — final operational decisions stay with your team.
Source preserved
Results stay linked to the image, location, and time they came from, so findings can be reviewed and verified.
Confidence shown
Where supported, detections and scores show confidence, and observations are labeled as observations — not as verdicts.
People decide
IntelCit recommends and explains; your team reviews, prioritizes, and decides what happens on the street.
Sample data
Possible litter detected · 87% confidence
Security, accessibility, and trust
A platform for public institutions has to be dependable in more than its analysis.
Organization boundaries
IntelCit is designed around organization-specific workspaces and role-based access. Production security hardening and verification remain release gates.
Accessibility by design
Designed toward WCAG 2.2 AA: keyboard access, visible focus, adequate touch targets, and status that never relies on color alone.
Two languages, real RTL
English and Persian ship from the same platform, with a true right-to-left layout — not a mirrored afterthought.
Honest labeling
Every service carries its real delivery stage, and anything illustrative is labeled as sample data. We do not claim readiness we have not demonstrated.
See IntelCit with your own streets
We run guided demo sessions for municipal teams, investors, and partners using the working MVP and clearly labeled sample data.
During the current pilot phase, demo sessions are arranged directly by the IntelCit team. The link below prepares a request in your email app without sending it automatically or inventing a contact address.
Your email app will open with a draft and no recipient. Add the IntelCit contact you already work with, review the message, and send it yourself.