Pipeline Integrity Software · AI

Pipeline integrity software
that reasons over your ILI data.

Turn the inline-inspection, dig, and defect data you already export into a prioritized, defensible integrity program. Cluster interacting defects, spend the dig budget where it actually reduces risk per dollar, and keep the reasoning attached for the regulator.

Lumina Express is the reasoning layer for organizational intelligence. Integrity is where it was first proven, on the millions of ILI rows your senior engineers can no longer read by hand.

The Last Mile of Integrity

The fastest integrity gains come from scaling the judgment your team already has.

You have terabytes of inline-inspection data and engineers who know exactly how to read it. The gap is time. A single run can take weeks to work through by hand, so dig programs get built under pressure and the audit trail gets reconstructed after the fact.

2M+
Sensor rows per typical ILI run
2 weeks
Typical time from ILI data receipt to dig program, done by hand
27%
Maintenance cost reduction with condition-based approaches (McKinsey)

What You Get

A defensible program, not a longer to-do list.

The point is not more analysis. It is a prioritized integrity program your team can defend, built from the data you already have.

A prioritized dig list, not a spreadsheet backlog

Turn the inline-inspection, dig, and defect data you already export into a ranked program. Interacting features get clustered and scored together, so the highest-risk anomalies surface first instead of hiding in millions of rows.

Dig budget that buys real risk reduction

Spend the excavation budget where it lowers risk per dollar, not where the easiest features happen to be. A cost-benefit view across the full set of anomalies tends to cut unnecessary digs while keeping the critical interactions in scope.

The reasoning attached for the regulator

Each burst-pressure result, interaction-rule application, and defect classification arrives with the work behind it, computed and verifiable, so your CER and PHMSA decision trail is built as you go instead of reconstructed from emails.

Engineering judgment that stays with the company

The interaction rules, corrosion models, and integrity playbooks live in the platform as logic you own, so the standard your senior engineer set holds across runs and segments instead of leaving on a retirement date.

Why It Is Different

Built for the way integrity engineers actually reason.

Generic analytics tools query rows. Pipeline integrity is about how defects interact, where the budget goes, and whether the decision holds up. These are the capabilities that difference depends on.

Defect-interaction clustering

Features that interact are evaluated as a group, the way a senior integrity engineer would, so two moderate defects sitting close together are caught as the single high-consequence interaction they form, not filed as two routine anomalies.

Dig optimization across the whole program

Priorities are weighed across safety factor, available outage windows, and regulatory deadlines at once, so the program bundles the right features into the right windows and right repair methods, rather than over-excavating low-risk ones.

Reasoning over millions of ILI rows

A full ILI run, with its millions of sensor rows, is reasoned over in the time it takes to ask the question. One engineer can run the integrity program for a system that used to need a queue of manual analysis to move at all.

Emerging-risk detection between runs

Growth-rate acceleration, corrosion clustering, and coating-and-soil interactions are watched across runs, CP surveys, and coating assessments, so a segment trending toward trouble is flagged before the next inspection cycle, not after.

These run on the same reasoning layer behind each Lumina Express agent. The Dialogue Intelligence Framework lets you arrive at the answer by asking, the Boardroom resolves dig-priority calls across integrity, operations, and compliance, and Lumina Cortex keeps each resolved decision as organizational memory that compounds across runs and segments.

vs Generic Tools

A spreadsheet macro is not an integrity program.

General BI and ILI viewers show you the data. They leave the interaction analysis, the dig economics, and the audit trail to you. Here is where the difference lands.

Reading a full ILI run
Engineers sift millions of rows in spreadsheets; a single run can take weeks.
A full run is reasoned over in minutes, with interacting features grouped automatically.
Interacting defects
Interaction rules applied by hand, run by run, where someone remembers to apply them.
Interaction clustering runs across the program, so high-consequence pairs are caught consistently.
Building the dig program
Dig lists guess without cost-benefit, over-excavating low-risk features and missing interactions.
Digs ranked by risk reduction per dollar and bundled into real outage windows.
The compliance trail
Decisions documented in emails and meeting notes, reconstructed when the auditor asks.
Each result carries the verifiable work behind it, so the CER and PHMSA trail builds itself.
Where the judgment lives
Models and thresholds live in one senior engineer and a tangle of personal spreadsheets.
Rules, models, and playbooks are owned, auditable, versioned logic that stays with the company.

FAQ

Pipeline integrity software, answered.

What is AI pipeline integrity software?

It is software that reasons over your inline-inspection, dig, and defect data to produce a prioritized, defensible integrity program. Instead of engineers manually sifting millions of ILI rows in spreadsheets, the system clusters interacting defects, applies interaction rules and burst-pressure assessment, and ranks digs by the risk they reduce, with the reasoning attached for the regulator.

Does it work with the ILI data we already export?

Yes. It is built to take the inline-inspection, dig, and defect data you already produce and turn it into a ranked program. You do not need to move to a new inspection vendor or change how runs are collected to start getting prioritized output from the data on hand.

How does it help us spend the dig budget better?

It evaluates anomalies across the whole program with a cost-benefit view, weighing safety factor, outage windows, and regulatory deadlines together. That tends to cut unnecessary excavations while keeping the high-consequence interactions in scope, so the budget goes where it lowers risk per dollar.

Is the output defensible for CER and PHMSA?

Each burst-pressure result, interaction-rule application, and defect classification is computed and verifiable rather than guessed, and the work behind it is preserved. The decision trail is built as your team works, so the auditable record regulators expect exists by construction instead of being reconstructed later.

Where does our data and engineering logic live?

Your operational data is analyzed where it already lives, and your interaction rules, corrosion models, and integrity playbooks stay yours as owned, auditable, versioned logic. The reasoning the platform produces accumulates as an organizational asset through Lumina Cortex, so the standard holds across runs and outlasts any single engineer.

Bring us one ILI run.

In a 30-minute session, bring a run and a hard segment. We will cluster the interacting defects, rank the digs by the risk they reduce, and show you the reasoning a regulator could read.

Book a Demo

Or see how the agents reason in the midstream integrity use case.