99.9% Uptime on Revenue-Critical Systems Across Every Location
A national entertainment operator went from finding out about outages when guests complained to catching 95% of issues before anyone was affected — holding 99.9% uptime across 500+ revenue-critical components.
What they were up against.
For an entertainment operator, every minute of downtime is visible, immediate, and expensive — queues back up, transactions fail, guests leave. This operator had grown its system footprint fast: POS terminals, ticketing kiosks, access-control panels, network hardware, digital signage, AV systems, and custom applications. Each was monitored independently, if at all. They needed one view across 500+ components — something that could watch everything at once, correlate events across systems, and surface the cause, not just the symptom, before it hit the floor.
- POS, kiosks, access control, network, signage, AV, and custom apps — each monitored separately, if at all
- Correlating a failure across systems took hours of manual investigation
- Problems surfaced only after the guest experience was already damaged
- Teams could see symptoms, never the root cause
What changed.
In the first 90 days after go-live, the operations team went from learning about problems after guests complained to catching 95% of issues before any guest was affected. Mean detection time dropped below 60 seconds. In the first full year, zero guest-impacting system failures were recorded, and uptime across all revenue-critical components held at 99.9%. Delivered as a Value Sprint and run through AI Office.
The system.
Unified telemetry stream
Custom telemetry agents plus existing monitoring protocols pull health data from every component type into a single normalized stream.
Cross-system correlation engine
Identifies cascading failure patterns — 'network degradation in Zone 3 is causing POS timeouts on terminals 12–18' — and triggers the right response workflow automatically.
NOC-style operations dashboard
A single view across the entire system estate, with pre-built response playbooks for every failure pattern the team had seen.
Automatic SLA escalation
Escalates on its own when response SLAs aren't met — no one has to be watching for it.
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