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 reviewable observations, clearer priorities, and practical next steps — 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
Five 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
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 interface preview — 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.
Possible litter detected · 87% model confidence — illustrative sample, not a validated performance metric.
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.
Evidence stays reviewable
Each response remains connected to its source and known boundaries, so teams can evaluate what the system returned without inflated claims.
See IntelCit with your own streets
We run guided demo sessions for municipal operations teams, investors, and partners to show the connected service flow from input to visible response.
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