Turn your existing traffic cameras into Vision Zero funding evidence.
AI safety audits for intersections — no new hardware, no surveillance, grant-ready in weeks.
[01] The problem
Cities are mandated to eliminate traffic deaths — but can’t afford to study every intersection.
So they guess which ones to fix — and then struggle to justify the spend to council and grant programs. Without evidence, safety dollars chase the loudest complaint instead of the highest risk.
[02] How it works
No new hardware. No new cameras. Just evidence.
Use your existing cameras
Point Zero Crash Lab at the traffic and CCTV feeds you already run. Nothing new gets installed.
AI detects near-misses & conflicts
The model surfaces conflict patterns and near-miss geometry — the leading indicators that show up long before a crash report does.
A grant-ready audit
You get a report that tells you which fix each intersection needs — and the evidence to fund it.
[03] The deliverable
One audit. Three answers your council actually asks.
Where conflicts cluster, and what kind — turning movements, pedestrian exposure, speed.
The countermeasure the evidence points to — matched to the pattern, not a generic checklist.
The expected crash reduction, framed to drop straight into a grant application.
[04] The funding case
The evidence your Vision Zero funding case needs.
Reviewers want demonstrated risk and a defensible expected benefit. Zero Crash Lab gives you both — observed conflict evidence paired with published crash-reduction factors, formatted for the application. For a Canadian municipal pilot, Ontario's OVIN R&D Partnership Fund is the program that fits: it covers up to roughly one-third of eligible costs, subject to program eligibility, alongside an industry partner named on the application.
[05] Privacy by design
It’s not surveillance. It’s geometry.
Zero Crash Lab analyses how vehicles and people move through a space — not who they are. The objection dies here.
No facial recognition, ever.
No plate capture or lookup.
Not a ticketing tool. No citations.
No personal data leaves the analysis.
Privacy & data handling
What Zero Crash Lab does — and doesn’t — do with your camera feeds.
The system does not identify individuals or vehicles. It is not enabled to run facial recognition or automated licence-plate lookup as a core function.
Output is aggregate conflict geometry — risk grades, movement patterns, and scene descriptions — not “who.” This is not an enforcement tool and does not generate tickets or citations.
The architecture — aggregate, anonymized output with no identity fields — is a deliberate design choice made from the start, not a policy layered on afterward. Event logs contain conflict events, timestamps, and trajectories only.
Your municipality owns all data generated from its own cameras, full stop. Zero Crash Lab processes it to produce the analysis — we don’t retain or resell it.
A formal Privacy Impact Assessment is the city’s process; we support it fully as part of the pilot data-governance agreement, drafted before any camera is connected.
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[06] Why it holds up
Built to satisfy your traffic engineer, not embarrass them.
Recommendations are grounded in published Crash Modification Factors — established, peer-reviewed crash-reduction evidence — and framed for engineering review. We report observed conflicts and known countermeasure effects, not speculative crash predictions.
[07] See it for yourself
Watch the model read an intersection.
Full intersection view, normal traffic flow.
Risk climbs SAFE → CAUTION → DANGER as the conflict develops, then CRITICAL.
In this recorded clip, the developing collision is detected and escalated for human review — an illustrative render of the audit’s conflict timeline, not a live alerting system.
Illustrative clips built on NVIDIA’s open-weight physical-AI (Cosmos world model) stack, self-hosted on GPU infrastructure — not a live, always-on feed.
Download a sample audit
See exactly what your council would receive — the danger, the fix, and the funding case for one intersection.
Opens an email to us — hit send and we’ll reply with the sample audit PDF.
[08] How to engage
Start with one intersection. Scale when it earns it.
One or two intersections, fully audited
A 90-day pilot on your own footage, sites selected jointly with your team, ending in a grant-ready report — before any long-term commitment.
- ✓ Uses your existing camera feed
- ✓ Danger / fix / funding report
- ✓ Delivered in weeks
Your whole network, watched
Ongoing conflict monitoring across intersections, so you catch emerging risk and keep your evidence current for every funding cycle.
- ✓ Multi-intersection coverage
- ✓ Refreshed evidence each cycle
- ✓ Priority ranking across the network
Pilot first — no long-term commitment required.
[09] Questions
The real questions.
Most existing fixed traffic and CCTV cameras with a usable view of the intersection — no new hardware required. Each camera needs a one-time calibration (roughly 2 minutes) before it can be analysed.
We analyse conflict geometry only — no faces, no licence plates, no personal data. Aggregate, anonymised safety events only; no raw footage is stored beyond the operational window; everything is processed and stored in Canadian jurisdiction, aligned with PIPEDA and MFIPPA. Full detail lives on the Privacy & Data Handling page.
Findings are grounded in observed conflicts and published Crash Modification Factors, framed for engineering review rather than speculative prediction. On a 20-minute normal-traffic benchmark, the shipped two-tier engine produced zero false near-miss and zero false collision alerts, with roughly six stopped-vehicle events per hour routed to human review rather than auto-alerted, and correctly caught both real events in our test set — a red-light near-miss and a box-truck T-bone crash. Night and rain performance has not yet been re-validated on the current engine.
Most municipalities start with a low-commitment pilot, then fold ongoing monitoring into standard procurement.
Zero Crash Lab is built by Grid Inc., a Canadian company. Founder Krishna Dogra is a full-stack developer & AI engineer based in Toronto.
[10] Book a pilot
Pick one intersection. We’ll show you what your cameras already know.
For small and mid-size municipalities ready to put evidence behind their safety spend.
Almost there.
Your email client should have opened with your request pre-filled — send it and we’ll reply personally to scope your pilot intersection.