In the early days, before we began to derisk Fortune 500 customer networks, our methodology was met with heavy scepticism from internal data science teams. This is because we can accurately predict and guide prescriptive maintenance. Some pushed back with justifications for why they couldn’t replicate our outcomes. Others quietly resisted, perhaps out of concern that our predictions might make their roles obsolete.
We don’t replace data science teams; instead, we add greater value by enabling them to solve problems that have previously been too complex, too rare, or too unpredictable to address.
| Proof, Not Promises
The defining test of our approach is simple: we predict what happens before it does.
For example, our blind tests correctly identify locations of gas leaks and accurately grade them across a year of data that we were never given access to. These applications and the ability to forecast are no longer theory. Put simply, real-world validation under blind conditions. No data contamination. No excuses. Just results.
Importantly, we are able to 'lift and shift' the same solution to other operators.
We have accelerated more use case examples that help guide operational decisions and influence capital replacement strategies.
Customers have told us about the value they place on strong links between science from the University of Florida, domain expertise, and solutions that speak to their real-world challenges. Particularly underground assets.
We follow an operational pathway pioneered by American companies that delivers accurate predictions 3–6 months ahead of what can be achieved by internal teams. This isn’t a critique of their talent; instead, it’s a reflection of the limitations they face with today’s tools, noisy datasets, reactive processes, and rare events.
We work with companies that are serious about derisking their network, optimising performance within a regulatory framework. Think: pipeline ruptures, outages, and inquiries. When millions of dollars and safety are at play, internal teams are now being challenged to replicate accurate forecasting, under similar conditions, clean separations, and strict controls over the data, blind testing results, and reviewed by the asset owners.
Placing the burden of proof on those doing the forecasting is challenging accepted modelling techniques that have been around for decades.
Our approach doesn’t disrupt workflows. No massive data extractions. We begin with a short discovery to assess what’s already accessible, cutting through the noise to deliver asset-level risk and failure probabilities. This is an 8-week process, and a fixed-fee, leading to blind testing and definitive proof. Your team verifies our predictions across data that we were never shown.
Our gas network operator customers have thousands of miles of ageing, buried assets. Predicting failure isn’t about innovation; it's risk mitigation, revenue protection, and public trust.
We provide this assurance across disparate networks, built over time, to different standards, materials, and climatic conditions, we are providing a holistic view and a greater understanding of networks.
An alternative, ask your internal team to deliver 3–6 months forward failure predictions validated through blind testing. It's that simple.
Let’s talk, if derisking your network, operational foresight, and cost efficiency matter; this conversation is overdue.