Imrandd launches AI-powered asset management solution for offshore operators
(WO) – Industrial data and engineering consultancy Imrandd has developed an asset management solution which harnesses artificial intelligence (AI) to reduce inspection time and costs for offshore operators.
Following a £1million investment, the company’s propriety monitoring and intervention ALERT software has been designed to go beyond traditional approaches to routine asset integrity monitoring. To support this latest innovation, Imrandd also received funding from the Net Zero Technology Centre (NTZC).
The Aberdeen-headquartered firm’s event-driven software monitors for corrosion threats allowing organizations to act before damage occurs, analyzing integrity data in near real-time. The integrated dashboard displays a live, round-the-clock snapshot of an asset’s performance to provide instant insights into its current condition while forecasting the future states of safety-critical systems.
Officially launched this week, ALERT is Imrandd’s fifth product adding to its suite of digital tools developed for the energy and industrial sectors. The company’s dedication to innovation has seen its R&D and data division more than double over the last two years, taking the team’s head count to 13 and the company’s overall workforce to 63.
Imrandd founder and CEO Innes Auchterlonie said, “We are committed to empowering asset owners and operators with exceptional tools to meet their asset integrity needs, it’s what our R&D team thrives on. Following a successful pilot program which delivered 40% efficiencies, ALERT has surpassed all our expectations. We are already in discussions with two UK operators about the transformative impact and value our new propriety software brings.
“The ability to instantly model and predict asset condition enables ALERT to serve as a leading indicator to proactively address potential threats before they escalate. System-agnostic, it reduces engineering time by allowing operatives to view asset systems against ambient conditions, degradation hot spots and remaining life cycle durations.
“Tens of millions of sensitive data points can be rapidly processed to provide more accurate trends and quality of output. Its intuitive approach replaces conventional ‘check-and-act’ methods with a more precise system enhancing efficiency, reducing downtime, and ultimately contributing to a safer and more sustainable future.”
