Real problems, real analysis. The kinds of decisions ML.ai helps integrity teams make — and the stories we share on LinkedIn.
An operator was inspecting on a fixed calendar. We built a quantitative risk model across the system to rank segments by probability and consequence of failure.
Result: assessments re-sequenced onto the highest-risk segments first — same budget, more risk retired.
Thousands of ILI anomalies, limited time. We modeled corrosion growth and remaining life to separate features that need action now from those that can safely wait.
Result: a defensible, ranked dig list instead of a flat threshold cut.
We trained predictive models on historical integrity and operational data, then made every prediction explainable down to the contributing factors.
Result: model insight your team can defend, line by line.
We implemented a PHMSA-compliant qualitative and quantitative risk analysis — transparent inputs, transparent scoring, fully documented.
Result: a risk model the operator can explain and defend on demand.
Using the geospatial data store, we enriched the analysis with location-based factors that a tabular model alone would miss.
Result: risk that reflects where the pipe actually is, not just what the table says.
A manual, error-prone analysis was rebuilt as an automated workflow where every number traces back to its source data and method.
Result: the same answer every cycle, produced in a fraction of the time.
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