The Customer: An Industrial Facilities Group With Safety at the Core
Our client manages a portfolio of facilities - warehouses, manufacturing floors, and office complexes - across multiple locations. Fire safety compliance was met on paper (sprinkler systems, smoke detectors, extinguishers), but the team was acutely aware that passive detection systems only work after a fire has already grown large enough to trigger a physical sensor. They wanted something smarter - a system that could see a threat forming before it became an emergency.
The Problem
Traditional fire safety infrastructure is reactive, not proactive. It waits for thresholds to be crossed.
- Physical Sensors Have Blind Spots and Delays: Conventional smoke detectors require smoke particles to physically reach the sensor unit. In large, open facilities like warehouses with high ceilings, by the time smoke reaches a ceiling-mounted detector, the fire below may already be significant.
- Human Monitoring Doesn't Scale: Security guards cannot watch every corner of every facility simultaneously, especially at night. Fatigue, distraction, and coverage gaps make human monitoring unreliable for a problem where seconds matter.
- No Multi-Location Visibility: The security team had no unified view across facilities. Each site was managed in isolation - there was no central system where a manager could see the status of all monitored locations at once.
- Alert Routing Was Manual and Slow: When an incident was spotted, the process of notifying the right people - facility manager, fire response team, building owner - was done via phone calls that sometimes took 10–15 minutes just to make contact.
How We Helped
We built a computer vision-based fire and smoke detection system that runs 24/7 across all camera feeds, detects visual signatures of fire and smoke in real-time, and triggers an automated multi-channel alert cascade the moment a threat is confirmed.
- Custom Vision Model Training: We trained a specialized detection model on a large, diverse dataset of fire and smoke footage across different lighting conditions, times of day, facility types, and fire stages - from a small smoldering source to open flames. The model is tuned specifically to minimize false negatives (missed detections) while keeping false positives below 2%.
- Real-Time Inference on CCTV Streams: The system integrates with the client's existing CCTV infrastructure - no new cameras required. It processes all feeds simultaneously in real-time, analyzing each frame for fire and smoke signatures with sub-10-second detection latency.
- Multi-Stage Threat Classification: Detections are classified by severity - early smoke, growing smoke, visible flames, confirmed fire. This allows the alert system to escalate appropriately and avoid overwhelming responders with minor early-stage alerts that security can verify first.
- Instant Multi-Channel Alerts: When a threat crosses the alert threshold, the system simultaneously notifies pre-configured personnel via SMS, push notification, email, and in-app dashboard alert - with the camera feed snapshot and location embedded in every message. No phone calls, no manual relay.
- Centralized Multi-Facility Dashboard: All camera feeds, detection events, alert logs, and system health indicators are visible in a single operations dashboard. A manager can oversee all facilities from a single screen, anywhere in the world.
The Results: Response in Seconds, Not Minutes
In the first year of deployment, the system identified three genuine fire events - all of which were caught and contained before sprinkler systems or physical detectors were triggered. Average detection time from fire onset to first alert: 9 seconds.
The false alarm rate held below 2%, meaning the operations team never experienced alert fatigue - every notification was treated as serious because the system had earned that trust. Night-time coverage, previously the weakest point of the client's safety posture, became their strongest.
