ML.ai Platform
Secure, web-based machine learning & analytics software built for integrity teams — no data science degree required.
PLR · Machine Learning for Asset Integrity
Machine Learning for Asset Integrity
We turn inspection and operational data into auditable, defensible decisions — combining 35+ years of hands-on integrity experience with modern, explainable analytics. Not a black box. Not another spreadsheet.
35+years of experience
40+modeled use cases
200k+pipeline miles analyzed
15+technical presentations
150+course attendees
What we offer
A complete integrity & risk analytics stack
Everything you need to go from raw data to a business decision you can put in front of a regulator — in one place.
Data-Driven Risk
A PHMSA-compliant qualitative & quantitative risk analysis process that stands up to scrutiny.
Model Library
Predictive models supporting 40+ common pipeline integrity and risk use cases out of the box.
Data Store
A low-cost geospatial data store that augments your analysis with the context that matters.
Why PLR
Built by integrity engineers
Most analytics vendors know data science but not pipelines. We've spent decades in the field, so the models reflect how integrity actually works — and every result traces back to the data and method behind it.
That's the difference between a number your team argues about and a decision your team — and your regulator — can stand behind.
Our story- 35+ years of real pipeline integrity experience
- Explainable models — never a black box
- PHMSA-aligned qualitative & quantitative risk
- Auditable, repeatable, traceable results
- Geospatial context built in
Our use of AI · The differentiator
One AI-learned ontology. Every use case.
Most analytics stop at a single model for a single question. PLR goes further with the Decision Graph Object (DGO) — an AI-learned ontology that maps how your assets, threats, data, and models relate, the way a knowledge graph captures a domain.
Build it once and it supports any integrity or risk management use case: threat analysis, risk scoring, prioritization, remaining life — all from one connected, reusable, and explainable structure.
Explore the DGO
In practice
Risk Prioritization
Corrosion Growth
Machine Learning
Real data, real decisions
Spend the integrity budget where it actually reduces risk
Rank segments by probability and consequence of failure so mitigations go to the highest-risk locations first.
Result: same budget, more risk retired.
Predict which anomalies actually matter next cycle
Model growth and remaining life to separate act-now features from the ones that can safely wait.
Result: a defensible, ranked mitigation list.
Explainable failure-likelihood models
Predictions explained down to the contributing factors — so engineers trust it and auditors accept it.
Result: insight you can defend.
Let's talk about your pipeline data
Book a short conversation — and we'll send you a sample analysis dashboard to explore.
Get startedNo pressure, no jargon — just a clear look at what's possible with your data.