See It Work
See It Work
SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+

SOLUTIONS · ASSET PERFORMANCE

Close the gap between asset insight and maintenance execution.

XMPro helps maintenance, reliability, and operations teams turn asset signals into prioritized interventions, coordinated work, and continuous reliability improvement before equipment issues become production loss.

THE PROBLEM

Predictive insight does not automatically change maintenance execution.

Industrial teams have spent years building analytics that explain or predict. The next step — turning those insights into decisions and actions that improve safety, reliability, quality, throughput, and cost — is still hard.

PROBLEM 01

Alerts don't fit the plan.

CBM and PdM findings generate alerts, notifications, and recommendations that don't align cleanly with the maintenance planning and scheduling process.

PROBLEM 02

Routine work eats the calendar.

Routine PM continues to consume capacity. Condition-based work becomes delayed or treated as break-in work.

PROBLEM 03

Better detection, same execution.

Teams pay for better detection, but the execution system still prioritizes the old routines it was built around.

THE SME BOTTLENECK

Scarce experts still carry too much of the decision load.

Asset performance still depends on a small number of reliability engineers and veteran technicians who interpret signals, diagnose failure modes, prioritize response, and translate recommendations into work instructions.

SYMPTOM 01

Decisions slow down.

When the SME isn't free, the call waits. Recommendations sit unread until someone with the context can interpret them.

SYMPTOM 02

Recommendations pile up.

Alerts and PdM findings accumulate faster than the experts can triage them, so the queue keeps growing.

SYMPTOM 03

Teams revert to routines.

Under pressure, crews default to the familiar PM calendar instead of the condition-based work that would prevent the failure.

How XMPro changes this

XMPro helps capture that expertise inside repeatable decision workflows and MAGS-powered agents, so asset insight can move into action more consistently.

THE XMPRO APPROACH

Connect asset context to prioritized 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 is not only earlier detection. The goal is a better decision loop.

STEP 01
Detect asset risk Surface emerging asset risk from live signals, history, and analytics before it shows up as production loss.
DETECT
STEP 02
Diagnose likely failure mode Apply MAGS-powered agents and trusted context to identify the failure mode most consistent with the evidence.
DIAGNOSE
STEP 03
Prioritize intervention Rank recommended actions by production impact, safety risk, parts availability, and operating constraints.
PRIORITIZE
STEP 04
Coordinate work Route the prioritized work into planning and scheduling so condition-based intervention enters the calendar cleanly.
COORDINATE
STEP 05
Capture evidence and outcome Record what was decided, who approved it, what was executed, and what happened next, in one reviewable trail.
CAPTURE
STEP 06
Improve the maintenance strategy Feed the evidence back into the next iteration so the policy, the agent, and the team all get better over time.
IMPROVE

AGENTIC MATURITY PATH

Move asset performance from monitor to bounded autonomy.

Each stage adds approved authority. Monitor & Predict surfaces risk early. Advise & Coordinate recommends action, prioritizes maintenance, and reconciles condition-based insight with planned work. Operate Autonomously triggers selected workflows within approved policy boundaries when confidence and governance are ready.

01 MONITOR & PREDICT

See early degradation

Detect early degradation, abnormal behavior, risk patterns, and performance opportunities.

02 ADVISE & COORDINATE

Guide the response

Recommend response, prioritize maintenance, reconcile condition-based insights with planned work, and coordinate action across teams.

03 OPERATE AUTONOMOUSLY

Act within boundaries

Trigger selected workflows or actions within approved policy boundaries when confidence and governance are ready.

RELEVANT AGENTS & TEMPLATES

Pre-built agents that operationalise the asset performance loop.

A curated selection of MAGS-powered agents and assistants that align with the asset performance maturity path. Browse the full marketplace for industry- and asset-specific patterns.

Take the deep dive

Transforming Asset Management through Advanced Predictive Maintenance.

A 30-page whitepaper covering the operating model, decision architecture, and execution patterns behind advanced predictive maintenance at industrial scale.

READY TO START

Close the gap on a single asset performance use case.

Pick one priority decision path. Connect the data. Govern the agent. Capture the evidence. Improve the strategy. Then expand.