Evidence before certainty
Probable sources are ranked with evidence and confidence. The product never disguises an inference as a fact.
Rillward · 2026Geospatial field operations application
Find the leak before the street tells you.
A geospatial web application using predictive AI to connect pressure anomalies, asset history, field crews, and service impact around the next decision.
Weak signals still need an owner.
Telemetry, GIS, work-order software, and customer reports are disconnected. Pressure loss can mean demand, sensor drift, planned work, or a burst, forcing teams to repeatedly rebuild the same operational story.
From pressure change to accountable response.
An exception-first web application combines a live network map, evidence timeline, confidence ranges, service impact, and a mobile-ready crew handoff. Predictive AI ranks probable causes, while a human operator confirms every response.
Three clear product targets shaped the work.
< 90 sec
Target from anomaly to accountable response owner
1 network
Pressure, assets, and service impact in one view
100%
Recommendations linked to evidence and confidence
Three principles keep the product clear.
Impact shapes priority
Service risk, vulnerable sites, and downstream disruption determine the queue, not alarm volume.
Field work completes the model
Crew observations, repairs, and rejected hypotheses return to the network history.
From pressure anomaly to evidence-led field action.
One response story now connects a weak signal to service impact, evidence, and field ownership without presenting an uncertain diagnosis as fact.
Predictive assistance remains trustworthy because every hypothesis is inspectable and every repair feeds the network history.
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Bring us the hard part.
Three quick questions. Then add your name, email, and company. We reply with fit and a practical next step.