ASSET PERFORMANCE · INSIGHT TO EXECUTION
Close the gap between asset insight and maintenance execution.
Turn asset signals into prioritised interventions, coordinated work, and continuous reliability improvement — before equipment issues become production loss.
Predictive insight does not automatically change maintenance execution.
Industrial teams have spent years connecting equipment, collecting sensor data, deploying asset performance tools, and building analytics that explain what happened or predict what may happen next. The next step is still hard: turning those insights into decisions and actions that improve safety, reliability, quality, throughput, and cost.
Alerts, notifications and recommendations fire across multiple systems — often without aligning cleanly with the maintenance planning and scheduling process.
Routine PM work continues to consume capacity. Condition-based work is delayed or treated as break-in work.
Teams pay for better detection, but the execution system still prioritises old routines.
Scarce experts still carry too much of the decision load.
In many plants, asset performance still depends on a small number of reliability engineers, rotating-equipment specialists, or veteran technicians who interpret signals, diagnose failure modes, prioritise response, and translate recommendations into work instructions. When those experts are overloaded or unavailable, decisions slow down, recommendations pile up, and teams revert to familiar routines.
XMPro helps capture that expertise inside repeatable decision workflows and MAGS-powered agents, so asset insight can move into action more consistently — even when the SME is on leave, on shift change, or stretched across multiple sites.
Connect asset context to prioritised action.
XMPro connects live asset data, maintenance history, failure context, production impact, parts availability, recommendations, workflows and MAGS-powered agents — so teams can move from reactive alarms to guided, coordinated asset performance decisions. The goal isn't only earlier detection. The goal is a better decision loop.
- 01
Detect asset risk
Live signals, anomaly detection and degradation patterns across every connected asset.
- 02
Diagnose failure mode
Match the signal to the failure mode, drawing on history, context and SME-codified rules.
- 03
Prioritise intervention
Rank by production impact, safety, parts availability and intervention window.
- 04
Coordinate work
Route into maintenance planning, parts, scheduling and the right team — not into another inbox.
- 05
Capture evidence & outcome
Record what was done, what worked, and what changed in the asset's condition profile.
- 06
Improve the strategy
Feed outcomes back into rules, models and maintenance strategy — the next decision is smarter.
From monitoring to autonomous operation — at your pace.
Customers progress along three operating phases as confidence, evidence and governance allow. Same canvas, same connectors, same governance — just more of the decision loop carried by the platform over time.
PHASE 1
Monitor & Predict
Detect early degradation.
Live asset signals, anomaly detection and risk patterns surfaced to the team that can act. Performance opportunities flagged before they become production loss.
PHASE 2
Advise & Coordinate
Recommend, prioritise, coordinate.
Recommend response, prioritise maintenance, reconcile condition-based insights with planned work, and coordinate action across reliability, planning and execution teams.
PHASE 3
Operate Autonomously
Act within policy boundaries.
Trigger selected workflows or actions within approved governance boundaries when confidence is high and policy allows — humans on the loop, not necessarily in it.
What asset performance looks like in production.
Ten asset-performance use cases customers run on the platform today — spanning predictive maintenance, condition-based work coordination, mobile equipment availability, and the supplier-risk context that keeps interventions on schedule.
Agent templates built for asset performance.
Six agent patterns that codify the decision work reliability and maintenance teams do today — ready to deploy from the AI Agent Library, customise to your operation, and govern under your control modes.
Equipment Health Monitor
Surfaces live condition, degradation patterns and health scores across the fleet so operators see the assets that need attention first.
AI AGENTFailure Investigator
Matches the observed signal to the likely failure mode using equipment history, OEM data and SME-codified rules.
AI AGENTMaintenance Reliability Strategist
Ranks open recommendations by production impact, safety, parts availability and the intervention window the team actually has.
AI AGENTSchedule Optimizer
Translates a recommendation into a work order, the right people, the right parts and a slot in the schedule — without sitting in another inbox.
AI ADVISORPump Predictive Maintenance Advisor
Asset-specific PdM advisor for rotating equipment — flags bearing wear, cavitation and seal degradation before failure.
AGENT TEAMAdvanced Predictive Maintenance Team
A team of cognitive agents coordinating across detection, diagnosis, prioritisation and work planning — the full decision loop, governed.
Trusted by industrial operators.
Where to go next.
Asset Performance sits inside the wider Agentic Operations Platform. Continue exploring the surrounding architecture.
Close the gap on your asset performance.
Bring asset signals, maintenance context, prioritisation, workflows and Expert AI Agents onto one canvas — under governance you control.
