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+

Operations Control Tower · WATER UTILITIES

Storm-water reservoirs that schedule themselves against the forecast.

Metropolitan flooding rarely fails on a missing sensor — it fails on a reservoir near capacity at the wrong time. The XMPro AO Platform fuses reservoir level, weather forecast and water-quality data into a single operating signal so reservoir capacity is managed against the next rain event, not the last truck-roll schedule.

THE CHALLENGE

What's getting in the way today.

Storm-water reservoir management fails across three reinforcing pressures:

ISSUE 01 OPEN

Accurate flood prediction

Forecast, hydrological and geographic data sit in separate systems, so flood-event timing, location and severity are hard to call ahead of impact.

ISSUE 02 OPEN

Effective response planning

Without dynamic crew dispatch and barrier activation tied to reservoir state, response runs late and resources land in the wrong place.

ISSUE 03 OPEN

Public safety and communication

Residents and businesses in affected zones need consistent, timely alerts — not retrospective communications after the incident.

THE SOLUTION

Storm-Water Reservoir Monitoring — how it works.

Continuous monitoring of reservoir level and water quality, fused with weather prediction and tied to dynamic crew dispatch — so reservoir capacity is managed against the next event, not the last schedule.

Real-time data integration Predictive analytics Anomaly detection Automated recommendations Operational dashboards Closed-loop control

The platform integrates meteorological, hydrological and geographic data with reservoir level telemetry and water-quality sensors. A predictive flood model combines current weather-station data with historical reservoir levels to forecast water-level trajectory and flood likelihood by location and severity. When current levels exceed predefined risk thresholds, governed action paths can automatically trigger water-release procedures via the action agent. Crew dispatch is scheduled dynamically against reservoir state and weather, not fixed routes. Water-quality monitoring flags industrial runoff. Automated alerts route to operators, emergency responders and the public communication channel; configurable dashboards and reporting support post-event analysis and regulatory compliance.

SEE IT IN YOUR ENVIRONMENT

Scope this for your operation.

Tell us about your fleet, your control maturity and the lever that matters most. We’ll map this use case to your starting point.

WATCH

Storm-Water Reservoir Monitoring — explainer.

WHAT CHANGES

What this looks like in operation.

Reduced flooding incidents

Proactive reservoir-capacity management against forecast minimises residential flooding rather than responding to it.

Safer crew dispatch

Dynamic truck rolls based on reservoir state and weather keep crews out of hazardous zones during predicted events.

Better environmental protection

Continuous water-quality monitoring exposes industrial runoff while it’s still recoverable.

DEPLOYED IN

Built for these industries.

PRODUCTION-PROVEN

Not a concept. In production.

XMPro is deployed at Tier 1 global operators across asset-intensive and mission-critical industries — delivering measurable results across predictive maintenance, process optimisation and operational intelligence.

VERIFIED RESULT — OIL & GAS
$16M Saved every year
18% Reduction in field service trips
95% Reduction in maintenance planning

Customer Case Study

Using XMPro, a global oil and gas supermajor rapidly composed and deployed an intelligent oil well maintenance solution in just three months -- achieving over $8 million in calculated value within the first six months.

VERIFIED RESULT — MINING
$10M Saved every year
30% Reduction in conveyor downtime
9,000t Saved every month

Customer Case Study

Using XMPro, the world's largest potash mining company rapidly composed and deployed a predictive maintenance solution for over 50 miles of underground conveyors in just 30 days, achieving $10 million in savings every year by reducing unplanned downtime by over 30%.

VERIFIED RESULT — ENTERPRISE SCALE
6 Sites with in-house adoption
1,000+ Assets monitored
35+ Operational, tactical and strategic use cases

Customer Case Study

XMPro enabled the in-house engineering team at a major North American miner to independently compose 35 operational, tactical and strategic solutions across six sites, scaling to monitor and manage over 1,000 diverse critical assets.

"XMPro successfully triggered a real predictive maintenance alert for a Haul Truck that appears to have a Strut issue - This was particularly impressive, considering we have only deployed the development environment a few weeks ago"

-- Advanced Predictive Maintenance Lead, major global mining company

AUTONOMOUS OPERATIONS

Now pushing the frontier.

MAGS agents are achieving what no other industrial platform has demonstrated — sustained autonomous operations at enterprise scale.

0+
Days Autonomous
Safety-critical petrochemical operations
3-0+
Agents Per Team
Specialized agents coordinating per use case
0+
Teams Deployable
Scale across sites and business units
0%
Governed
Every agent, every decision, every action — auditable

SCOPE FOR YOUR SITE

Let’s scope this for your operation.

Talk to an XMPro engineer about your environment, your starting HAS level and the lever that matters most — or browse more solutions.