Predictive signals
Failure risk scored from sensor, alarm and maintenance history with lead time to act.
Manufacturing · Case Study
Operations Copilot
An operations copilot that surfaces risk, recommends actions, and coordinates maintenance and production teams.
- 37%less unplanned downtime
- 2×faster mean-time-to-repair
- 9 moto full two-line deployment
Delivered
What we built
Reduced downtime by 37% with predictive agents on the plant floor.
Work-order assist
Prioritized orders raised in the CMMS with context and suggested parts.
Technician copilot
Manuals, history and guided troubleshooting at the machine.
OT/IT integration
Historian data flowing safely into IT analytics without touching control systems.
Inside the build
The line that stopped surprising everyone
An industrial manufacturer's two flagship lines lost a shift a week to unplanned stops. The copilot turned reactive firefighting into scheduled work.
The challenge
Maintenance by emergency
Failures announced themselves by stopping production. Thirty years of expertise lived in retiring heads, maintenance logs were inconsistent free text, and the CMMS was a filing cabinet rather than a tool.
- A shift per week lost to unplanned stops
- Expert knowledge walking out the door
- CMMS data rich but unused
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
The solution
Prediction wired into the way work happens
Models trained on historian and work-order data score failure risk with actionable lead time — and raise prioritized CMMS work orders automatically. At the machine, technicians query manuals, history and past fixes conversationally.
- Risk scores with 2–14 day actionable windows
- Auto-raised work orders with parts suggestions
- Copilot answering from manuals and past repairs
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
The impact
Downtime fell, knowledge stayed
Unplanned downtime dropped 37% in nine months; MTTR halved as technicians stopped diagnosing from scratch. The copilot became the de facto archive of the veterans' knowledge — searchable, forever.
- 37% unplanned downtime reduction
- MTTR halved with guided troubleshooting
- Tribal knowledge captured and queryable
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Outcomes
What changed on the floor
Output
Recovered capacity equal to half a shift weekly.
Maintenance planning
Emergency work converted to scheduled work.
New technician ramp
Juniors performing with veteran guidance on demand.
Parts inventory
Stocking aligned to predicted failures.
Safety
Fewer emergency interventions on live equipment.
Scale-out
Blueprint now deploying to four more lines.
Delivery
From challenge to outcome
We publish the architecture decisions, delivery approach and measurable results — so you can see how similar outcomes could apply to your enterprise.
- Measurable KPIs
- Architecture transparency
- Cross-industry patterns
- Repeatable playbooks
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1
Challenge
Business pressure, legacy process and constraints.
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2
Solution
Architecture, agents and integration design.
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3
Delivery
Build, evaluate, secure and launch.
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4
Impact
Efficiency, cost, quality or revenue outcomes.
The predictive alerts pay the bills, but the copilot is what the floor loves — it's like having our best retired engineer on every shift.
Under the hood
The stack behind the copilot
OT data flowing one-way into cloud analytics; nothing touches control systems.
- OSIsoft PI
- Maximo
- Azure IoT
- Databricks
- Python
- Anthropic Claude
- Kubernetes
- Grafana
- Kafka
- Terraform
FAQ
Common questions
Did this require new sensors?
No — existing historian data carried enough signal for the first models. Sensor additions came later, targeted at the assets where prediction gaps justified them.
How did you handle messy maintenance logs?
Entity resolution and extraction over free text — inconsistent logs are the industry norm, and mining them is where much of the veterans' knowledge actually lived.
Is our OT network at risk?
One-way data flows from the historian through approved gateways; no writes toward control systems, network segmentation respected throughout. The OT team held veto power at every design review.
Next step
Want your lines to stop surprising you?
Pick your most painful asset — we'll assess predictability from the data you already have.