Watching a physical fleet before small problems get expensive.
A confidential engagement connecting a distributed fleet of physical assets into one monitoring platform — real-time status, maintenance tracking and an AI agent that reads the telemetry for early warning signs.
Confidential client engagement. Client details are withheld.
The challenge
The client operates physical assets across multiple locations, and maintenance was largely reactive — issues were caught after they caused downtime, not before.
What we built
An IoT platform ingesting live telemetry from distributed equipment into real-time fleet dashboards with location and status information, historical analytics, and maintenance tracking. An AI operations agent continuously analyzes incoming telemetry, watching for the early signals that typically precede a failure, and surfaces them as maintenance recommendations before they become costly downtime.
How it's built
AI & automation capabilities
Key features
01 · Monitoring
Real-time status across a distributed fleet
Live telemetry from distributed equipment feeds fleet dashboards showing location and status in real time, not a periodic manual check-in.
02 · AI agent
Reading telemetry for early warning signs
An AI operations agent continuously analyzes incoming telemetry for the early signals that typically precede a failure, not just flagging a fault after it's already happened.
03 · Maintenance
Recommendations before downtime, not after
Predictive maintenance recommendations surface while an issue is still cheap to address, ahead of the point where it becomes unplanned downtime.
04 · Analytics
Historical analytics behind every alert
Every alert and maintenance recommendation is backed by historical analytics, so a manager can see the pattern that led to it, not just the alert itself.
Stack
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