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Secure, web-based machine learning & analytics software built for integrity teams — no data science degree required.
PLR · Analytics & Machine Learning for Asset Integrity
Analytics & Machine Learning for Asset Integrity
We take pipeline integrity and risk beyond compliance — turning the data you already collect into transparent, defensible decisions about where to spend, what to inspect, and how to reduce risk. 35+ years in the field, trusted by market leaders.
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
Beyond compliance — defensible, transparent, data-driven decisions you can stand behind.
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.
Our story- 35+ years of real pipeline integrity experience
- Beyond compliance — decisions that support the business
- Explainable, defensible results — never a black box
- Comparable rigor to the big firms, at a fraction of the cost
- Repeatable — the same answer every cycle, with a full audit trail
- A process you own — the analysis doesn't walk out the door
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: inspection prioritization, compliance, threat analysis, direct assessments, 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.