Asset Health Monitoring
AI scores each machine for failure risk from its sensor data. Your service team decides the action.
See how it worksIndustries · Manufacturing · Predictive maintenance workflow
Fix it before it fails.
Every hour a machine is down costs you and your customer. We help your team catch problems early and fix them on the first visit.
The problem
Where the predictive maintenance workflow slows down.
Each stage waits on the one before, and your customers wait longest.
Detect
Sensor data streams in, but few alerts come before a failure.
Diagnose
Engineers search manuals and past tickets by hand to find the fault.
Work order
Work orders are written by hand, often missing parts and steps.
Dispatch
Technicians arrive without the right part, so the visit repeats.
Seeing a failure early changes every step after it. That is where we start.
The workflow
One workflow, four agents, built one at a time.
A workflow is a chain of stages, and each stage needs its own AI agent. Predictive maintenance has four stages, from the first warning sign to the technician on site. We map the agent each stage needs with your team. We build the first agent, take it live, then add the next.
- 1
Map the workflow
We walk through each stage with your team and name the agent it needs.
- 2
Build agent 1 and go live
We build Asset Health Monitoring first, test it on past failures and take it live.
- 3
Add the next agent
Each new agent reuses the same data, rules and templates, so it goes live faster.
The four agents in the predictive maintenance workflow
Fault Diagnosis
AI reads sensor data, service history and manuals, and names the likely fault. Your engineer confirms it.
Work Order Automation
AI drafts the work order with the fault, parts and repair steps. Your planner releases it.
Service Dispatch
AI matches each job to a technician with the right skills and parts. Your dispatcher approves it.
Agent 1
Asset Health Monitoring
Every machine scored for failure risk each shift. Your service team reviews each alert.
Sensor data joins each machine's service history on your data platform. That can be Azure, Databricks, Snowflake or Google BigQuery.
Sensors and historiansReadings through a secure gateway
Risk modelScores each machine every shift
Service team decidesThe action for each alert
Service systemVisit planned with the risk known
- 1
Collect
Sensor data streams through a secure gateway.
- 2
Join
Readings join each machine's service history.
- 3
Score
A model scores each machine for failure risk every shift.
- 4
Review
Your service team reviews high-risk alerts and decides the action.
- 5
Learn
Each repair result feeds back into the model.
IT and OT networks stay separate, and every alert and action is logged.
Use cases
What connected equipment data makes possible.
Every alert and repair is saved with its result, for other teams to use. Each card shows the data it needs.
Service and installed base
Know every installed unit, its history and its next service.
NeedsInstalled baseService tickets
Spare parts planning
Stock the parts for the failures you expect.
NeedsFailure historyParts
Service contract margin
Track uptime and cost by contract.
NeedsService costContracts
Product design feedback
Show engineering which parts fail most, and why.
NeedsFailure historyBills of materials
The same data also feeds your service and quality reports. Built on the manufacturing data foundation ›
Equipment makers
What this workflow can deliver
Typical results for predictive maintenance, based on industry averages.
Typical results
Typical
20%
Less unplanned downtime
Typical
24 hours
Or more of warning before a failure
Typical
15%
More fixes on the first visit
Typical
40%
Less time writing work orders
We will add measured client results as the work goes live.
Questions
Questions service leaders ask us.
Do we need all four agents?
No. Each agent works on its own. We map all four so they share data and rules. Then we build them one at a time, fastest payback first.
Which data platforms do you work with?
Azure, Databricks, Snowflake and Google BigQuery. We build on the one you run.
Equipment and IoT platforms include AI monitoring. Why build ours?
Platform tools watch one vendor’s machines. Ours combines sensor data with service history, parts and manuals. You own the models and the data.
Which equipment data do you work with?
Sensor data over OPC UA and MQTT, plant historians, and your service system.
How do you keep OT systems secure?
IT and OT networks stay separate. Data leaves through controlled gateways, and both are monitored.
Who decides what happens on an alert?
Your service team. AI ranks the risk, and a person decides the action.
Get started
Find where AI pays back first in your service operation.
4 minutes
AI Readiness Diagnostic
Ten questions about your data, systems and teams. You get a score and a clear place to start.
Take the AI Readiness DiagnosticDownload a sample report ›2 to 3 weeks
Data Readiness Assessment
Our data engineers score your data and give you a ranked plan for the first projects.
Book a Data Readiness AssessmentDownload a sample report ›5 days
AI Use Case Prioritization
Your AI ideas scored and ranked, with the right agent, model and size for each.
Book AI Use Case PrioritizationDownload a sample report ›