Ayn · Pitch Deck
Smart City
Aynعين المدينة
Traffic, parking, incident detection, and crowd flow — on edge devices.
neuriya.ai — Sovereign, Arabic-first enterprise AI
Problem
Cities detect incidents, parking gaps, and crowd risk after the fact, not as they happen
- Traffic collisions, stalled vehicles, and unusual crowd gatherings are typically caught by manual patrol or a citizen phone call, not by the camera network already watching the street.
- Parking occupancy is a static count from the last inspection, so drivers and enforcement teams are working from data that's already stale.
- Crowd density and movement patterns go unmonitored until a complaint comes in or a gathering has already become a safety concern.
- Municipalities already operate large CCTV networks, but that footage is used for post-incident review, not turned into a live operational feed — and buying new sensors for traffic and parking is a slow, expensive procurement cycle.
Solution
Existing CCTV becomes a live feed of incidents, parking, and crowd risk
Product
Ayn in action
How it works
From first touch to outcome
Connect
Edge devices attach to your existing camera feeds without touching the camera itself.
Detect
Traffic, parking, incidents, and crowd flow are analyzed continuously at the edge.
Alert
Operators see incidents the moment they happen, with location and severity.
Report
Historical trends inform planning decisions beyond the live feed.
Market
Sizing the opportunity
- TAM — $1.5B. MENA smart-city computer-vision & urban-analytics market
- SAM — $150M. Saudi municipal CCTV-based traffic, parking & crowd analytics
- SOM — $10M. 3-year obtainable share via design-partner municipalities
Saudi municipalities have built out large CCTV networks under Vision 2030 smart-city programs, but most of that footage is still used only for security replay — turning it into live operational data is a budget case that doesn't require new hardware procurement.
Business model
How Ayn makes money
Per-camera / per-feed analytics license
Subscription priced by the number of camera feeds or lots under active monitoring.
Per-municipality platform subscription
Recurring fee for the city monitoring dashboard covering incidents, parking, and crowd-flow analytics.
Implementation & calibration fee
One-time fee for camera-feed integration and site-specific model calibration.
Revenue streams
Where the revenue actually comes from
Competition
Where we win
Our edge
- Runs on the camera network municipalities already have — no new hardware procurement cycle, fastest path to a pilot.
- Edge-first architecture keeps bandwidth and latency from bottlenecking a citywide rollout.
- Sovereign, PDPL-aligned, NDMO-classified data residency — video and analytics stay in-Kingdom by construction.
- Founding team combines Saudi urban-infrastructure delivery experience with computer-vision engineering.
Traction
Early validation, built for pilot readiness
- Full city-monitoring product experience designed and built end-to-end: camera network dashboard, incident detection, parking occupancy, crowd-flow analytics.
- Architecture pre-aligned to Vision 2030 alignment, PDPL, and NDMO Classification.
- Actively selecting design-partner municipalities for a first pilot cohort.
Go-to-market
Land on one camera network, expand to citywide coverage
- 1Land with 1-2 design-partner municipalities, starting on a defined camera network or district.
- 2Prove incident-detection and parking-accuracy gains over a pilot quarter, then expand to full citywide coverage.
- 3Expand via MOMRAH and municipal-council relationships to additional cities and districts.
Team
Built by operators, not just builders
The ask
Raising a seed round to reach first municipal pilots
We're raising to fund computer-vision model hardening for traffic, parking, and crowd use cases, edge-deployment tooling, and the core team through first-revenue pilot deployments.