How AI-driven inspection is already changing gas pipeline and refinery integrity work

The idea that AI will eventually change infrastructure inspection is not a forecast - for gas pipeline and refinery integrity work, it is already underway, and the driver is a straightforward, structural problem: the assets keep growing and ageing faster than manual inspection review can keep pace with, and the part of the workflow eating the most time was never the flight, it was everything that happened to the evidence afterward.

The bottleneck was always the review, not the flight#

Capturing inspection evidence - by drone or any other method - is comparatively fast. Reviewing it is not. An experienced engineer manually comparing this cycle's evidence to last cycle's, across hundreds of kilometres of pipeline or dozens of pieces of refinery static equipment, is slow, inconsistent between reviewers, and does not scale as the asset base grows. That review-and-compare step is exactly where AI-assisted tools are having the most immediate, practical impact today - not by replacing engineering judgement, but by standardising evidence so a genuine change is easy to spot and by handling the first pass of comparison so an engineer's time goes toward the findings that actually matter.

What "AI-assisted" should actually mean#

A lot gets called "AI-powered" in inspection technology without much specificity. The version worth taking seriously does a few concrete things: it makes evidence from different inspection cycles genuinely comparable rather than requiring a person to recall what a segment looked like a year ago; it surfaces a candidate finding with the evidence behind it and a confidence level, rather than an opaque score; and it produces output structured for the compliance and reporting formats an integrity team already uses, instead of creating a new manual transcription step. None of that replaces the engineer's judgement - it removes the manual work standing between raw evidence and the point where that judgement gets applied.

Two real, current application areas - not one and a future one#

This shift is playing out in gas pipeline networks, including City Gas Distribution, where inspection-capacity growth has not kept pace with network expansion, and separately in refinery and industrial static equipment - fired heaters, pressure vessels, storage tanks, elevated piping - traditionally inspected via scaffolding or rope access. Both face the same underlying problem in different physical settings: too much asset, too little inspection throughput, and a prioritisation decision that has historically rested more on institutional memory than on structured evidence.

Where LeakSonic fits, honestly#

Sentrix is our answer to that problem: AI-driven decision-intelligence software, paired with drone hardware we design, build, and test ourselves specifically to keep that AI grounded in real flight data rather than a synthetic benchmark. We are explicit about what stage we are at - actively developing and validating the platform with practising engineers, not presenting unproven claims as fact - and explicit about what the platform does not do: it does not replace inline inspection, cathodic protection monitoring, or contact-based NDT, and it does not remove the engineer from the decision. If you want to see exactly how that applies to a pipeline network or a refinery site, the platform page walks through it in detail, or talk to us directly about your own network or site.