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MeshInsights
Where to start
with MeshInsights
These five outcomes come up most often across connected-product companies. If none of them match exactly, your actual starting point is whatever already costs your team the most expert judgment today.
Five Starting Points
Patterns worth recognizing.
Each of these reflects something teams already do by hand today. Treat them as patterns worth recognizing, not the only paths in.
1. Early Warning
Detects degradation, failure risk, and abnormal behavior before problems become downtime events.
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What it needs:
Asset telemetry, alarm history, and maintenance records showing prior failures.
2. Service Resolution
Turns an alert into a recommended action: remote fix or dispatch, parts needed, escalation path.
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What it needs:
Alert history, service tickets, and technician resolution notes.
3. Performance Management
Identifies waste, inefficiency, and capacity gaps across the installed base.
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What it needs:
Utilization data, energy consumption, and throughput or capacity benchmarks across the fleet.
4. Aftermarket Growth
Finds commercial moments inside machine behavior and surfaces them as revenue actions.
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What it needs:
Usage patterns, asset age and wear indicators, and prior purchase or replacement history.
5. Customer Health
Tracks relationship health through machine behavior: inactive assets, unresolved issues, churn risk.
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What it needs:
Account activity, support ticket history, and usage or engagement patterns tied to churn.
Don’t see your pattern above?
That’s fine. Plenty of starting points don’t look exactly like the above agentic decisions. What matters is whether the decision already happens by hand on your team today, however it’s shaped.Â
Early Warning
A multi-site commercial HVAC operator
Manually set power-alert thresholds were quietly wrong for half the year, burying real problems in noise that analysts had to review one by one. An Early Warning Agent now recalibrates every unit’s threshold monthly against actual operating data, and flags any unit without enough history for a reliable call. A two-week prototype validated the approach. Trusted alerting now runs directly inside the existing platform, with no new tools required.
Service Resolution
A global manufacturer of industrial steam system components
Thousands of IoT-monitored components generated alerts, but each one needed an expert to confirm the failure and initiate service. The monitoring outpaced the people. A Service Resolution Agent, deployed in four weeks, now applies that expert reasoning automatically: determining failure mode, assessing confidence, and creating dispatch-ready tickets. Over 95% of service events now resolve end-to-end with no expert involvement.
The five agentic use cases above are starting points, not a checklist. What matters more is whether the work already lives in your business today.
A few signals tell you more than which pattern it resembles:
Once the first agent is live, the next automation starts faster. Each additional outcome reuses the method, the evidence discipline, and the platform behind the first one, so the build lift goes down and the pace picks up as you add more.
Ready to pick your starting point?
Tell us about the outcome your team already produces by hand today. We’ll help you find the fit, whether it’s on this list or not.