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Banking estate alerts looked urgent but carried no context.
A banking network with 3,000+ locations needed security teams to know which alarm mattered before response time was lost.
Client
Large Banking & Financial Services Network
Duration
6 months
Industry
Banking & Finance
99.9%Uptime at Scale
<100msAlert Latency
3,000+Locations Covered
↓False Positives
Client problem
In their words
Across 3,000+ locations, thousands of sensors, cameras, and alarms generated overwhelming alert volumes with no prioritization. Manual triage delayed responses to real threats and inflated operational costs through alert fatigue. Everything was monitored. Nothing was contextualized.
What ZapSight built
One operating system around the failure mode
- Built an ambient incident layer above existing sensor, camera and alarm infrastructure
- Correlated multi-sensor and video signals into context-rich, prioritized alerts
- Automated escalation workflows with human-in-the-loop oversight
- Added predictive sensor health monitoring to prevent blind spots
- Centralized operations into a single security intelligence dashboard
Technologies Used
PythonComputer VisionKafkaEdge ComputePostgreSQL
"We moved from reacting to noise to acting on signal. Critical incidents now surface in seconds instead of hours.