Know exactly how many people are in your space, right now.
Safari AI turns your existing cameras into a live occupancy system. Track current occupancy by zone, trigger alerts at capacity thresholds, and stay compliant.
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Guessing at occupancy creates safety gaps, compliance risk, and missed revenue.
Most venues rely on rough estimates, dated badge-scan totals, or passive beam sensors to track how many people are inside. When those numbers are wrong, you're flying blind on staffing, safety, and capacity compliance.
- —Passive beam counters at doorways lose accuracy during crowds, rushes, or any multi-directional flow
- —Badge and ticket scans capture entries but never exits. Net occupancy is always a guess.
- —No zone-level visibility: packed areas go undetected until a complaint or an incident
- —Capacity violations discovered after the fact, not in time to act
- —Manual headcounts delay data by hours and pull staff away from guests
Validated against ground-truth manual counts at deployment. If a view underperforms, we retune before you go live at no cost.
Leverage your existing cameras. No construction. Live in under two weeks.
Camera Review
We assess your existing CCTV or IP camera feeds remotely. Compatible views proceed; incompatible ones are flagged before any commitment.
On-Prem Deployment
A compact server is installed on-site and connected to your camera streams. All video is processed locally. Nothing leaves your network.
Calibrate & Go Live
Models are validated against manual counts. Once accuracy is approved, you're live with real-time dashboards and API access from day one.
How leading operators use Safari AI occupancy data to drive decisions.
#2 Outlet Operator in North America
The second-largest outlet operator in North America uses Safari AI to track real-time occupancy across zones, including parking, guest dwell times, and restroom utilization, to optimize staffing and guest experience.
Read Outlet Operator Case Study
7-Eleven
7-Eleven measures real-time store occupancy and door-count conversion rates across 10+ NYC locations, enabling data-driven staffing decisions and property performance benchmarking.
Read 7-Eleven Case Study
Taco Bell
Taco Bell uses pedestrian and in-store occupancy analytics to evaluate potential new locations, using foot traffic and dwell data to make confident, data-backed investment decisions.
Read Taco Bell Case StudyEverything occupancy analytics should do, and actually does.
True net occupancy updated every second from any camera view.
Break down occupancy by floor, wing, section, or any custom zone.
Understand how long visitors stay by zone to optimize flow and staffing.
Compare utilization rates across locations to find patterns that drive decisions.
Capacity Threshold Alerts
Set custom limits per zone and receive instant alerts via SMS, email, Slack, or webhook when occupancy crosses a warning or hard-cap threshold. React before a safety or compliance issue escalates.
Entry / Exit Bi-Directional Counting
Track ins and outs simultaneously across every entrance and exit. Safari AI reconciles counts continuously so net occupancy stays accurate even during high-traffic rushes and multi-path flow.
BI & Access Control Integration
Push live occupancy data into Tableau, Power BI, Snowflake, or your access-control system via REST API. Safari AI slots into your existing analytics stack with no bespoke middleware required.
Historical Reporting & Compliance Exports
Access full occupancy histories with minute-level resolution. Export reports for fire-marshal inspections, lease audits, or internal capacity planning. Safari AI retains timestamped records on your behalf.
Frequently Asked Questions
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Safari AI delivers 99%+ accuracy on pedestrian and footfall counts across indoor and outdoor environments. Accuracy is validated against manual ground-truth counts during deployment, and our computer vision models are trained on enterprise-scale datasets from theme parks, stadiums, retail destinations, and QSRs. If a camera view underperforms, we tune the model to your specific environment before you go live.
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No. Safari AI works with the CCTV and IP cameras you already have — no camera rip-and-replace, no construction, no re-wiring. Deployment requires an on-premise server to process the video feeds locally at your site, which we spec and configure as part of onboarding. Your existing camera infrastructure stays exactly as it is.
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Most customers are live within days to a few weeks, depending on server provisioning and site access. After an initial camera review to confirm compatibility, we install the on-prem server, connect your existing camera feeds, calibrate the models, and validate accuracy against your baselines.
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Safari AI is built for high-density venues — we measure crowd counts and pedestrian flow at theme parks, NHL and NBA arenas, outlet centers, and stadium concourses. Our models handle occlusion, overlapping visitors, and non-linear movement patterns that break traditional sensor-based or beam-break counting systems. Reference clients include LEGOLAND, the Charlotte Hornets, and the Calgary Flames.
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Yes. Counts and analytics are available through live dashboards, scheduled exports, and REST APIs, which means you can pipe footfall data into Tableau, Power BI, Snowflake, your POS, or any internal system. Most enterprise customers run Safari AI alongside existing BI and RevOps workflows rather than as a standalone dashboard.
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Pricing is per-camera and scales based on the number of cameras, sites, and measurements you need — pedestrian counts, occupancy, dwell time, queue wait, and more can be layered on the same feeds. We offer a free 90-day pilot using your existing cameras with no credit card required, so you can validate accuracy and ROI before committing. Contact us for a tailored quote.
See exactly what your cameras can do.
Evaluate Safari AI on your existing camera infrastructure for 30 days. No credit card, no commitment.
30-day free pilot · No credit card required · Uses your existing cameras · Video processed on-premise
Frequently Asked Questions
How accurate is Safari AI's footfall counting?
Safari AI delivers 99%+ accuracy on pedestrian and footfall counts across indoor and outdoor environments. Accuracy is validated against manual ground-truth counts during deployment. If a camera view underperforms, we retune the model to your specific environment before you go live, at no additional cost.
Do I need to replace my cameras to use Safari AI?
No. Safari AI works with the CCTV and IP cameras you already have. No rip-and-replace, no construction, no re-wiring. An on-premise server is installed to process video locally; your existing camera infrastructure stays exactly as it is.
How long does Safari AI deployment take?
Most customers are live within days to a few weeks. After a camera compatibility review, we install the on-prem server, connect camera feeds, calibrate the models, and validate accuracy against your baselines before going live.
Can Safari AI handle high-density crowds?
Yes. Safari AI is built for high-density venues — theme parks, NBA and NHL arenas, outlet centers, and stadium concourses. Models handle occlusion, overlapping visitors, and non-linear movement that defeats traditional beam-break sensors. Clients include LEGOLAND, Charlotte Hornets, and Calgary Flames.
Can Safari AI integrate with Tableau, Power BI, Snowflake, or our POS?
Yes. Footfall counts and analytics are available via live dashboards, scheduled exports, and a REST API. You can pipe data into Tableau, Power BI, Snowflake, your POS, or any internal system. Most customers run Safari AI alongside existing BI and RevOps workflows.
How does Safari AI pricing work?
Pricing is per-camera and scales with the number of cameras, sites, and measurement types. Pedestrian counts, occupancy, dwell time, queue wait time, and more can be layered on the same feeds. A free 30-day pilot with no credit card required is available so you can validate accuracy and ROI before committing.
IR beam-break sensors are documented to miscount in high-traffic or wide-entrance conditions due to simultaneous crossings and non-human obstructions. Accuracy in crowd conditions can fall to 60 to 85%, representing a 15 to 40% undercount error. Sources: People Counting Systems — Infrared Sensors; V-Count — People Counting Technologies Guide; Milesight VS360 IR Sensor (up to 80% accuracy noted).