AI from elite motorsport

Binnies, Williams Grand Prix Technologies and JuliaHub announce groundbreaking partnership

Published

04 AUG 2025

Est. reading time

4 min

AI from elite motorsport: Binnies, Williams Grand Prix Technologies and JuliaHub announce groundbreaking partnership to bring scientific machine learning to the UK water sector for the first time

In a transformative move for the UK water industry, Binnies – a leading water engineering services provider – has announced an exclusive partnership with Williams Grand Prix Technologies, the engineering consultancy of the Atlassian Williams Racing Formula 1 Team, and artificial intelligence (AI) pioneer, JuliaHub. Together, they are introducing scientific machine learning (SciML) to the UK water sector for the first time.

This pioneering collaboration brings cutting-edge AI from the motorsport, aviation and aerospace sectors to help water companies shift from reactive to predictive asset health to prevent failures – using largely the data they already have. It directly supports the direction set out in the recently released Cunliffe Review, which calls for water companies to get ahead of the failure curve by proactively assessing asset condition, even when data quality is limited.

Responding to the AMP8 challenge

As the UK water sector enters its next five-year investment cycle (AMP8), both companies and regulators have made one thing clear: the industry must predict failures earlier and take proactive control of asset health. Yet many report that poor data quality continues to limit the effectiveness of existing predictive models.

A breakthrough from outside the industry

To overcome this challenge, Binnies looked beyond the water sector and formed a three-way partnership with Williams Grand Prix Technologies and JuliaHub – combining deep sector expertise, elite engineering performance, and world-leading SciML. Together, they’re introducing this new class of AI to the UK water sector for the first time – technology purpose-built for complex engineering environments where traditional machine learning falls short.

Traditional machine learning relies on large volumes of clean, historical data. When data is missing or poor quality, it struggles – unable to fill gaps or recognise failure modes it hasn’t seen before. This often leads to expensive sensor deployments just to improve model accuracy.

SciML takes a different approach. By combining physics with machine learning, it uses scientific laws – such as fluid dynamics and thermodynamics – to fill data gaps and model how assets should behave, enabling accurate predictions even without historical data or with only minimal sensors.

Already proven in top-level motorsport, aviation and aerospace, SciML enables water companies to extract predictive insights largely from the data they already hold – reducing the need for costly sensor deployments or large-scale data cleansing.

More than a new technology

This marks more than just a tech upgrade – it’s a mindset shift. The sector is moving from reactive to predictive operations, from lagging indicators to leading insights. By rethinking how existing data is used, risk is managed, and investment is targeted, this partnership unlocks a new level of operational resilience across the industry.

Validated and delivering

Early projects with Southern Water and Anglian Water have begun the validation of the technology, successfully showing that SciML can help unlock predictive asset health at pace and scale. This approach supports the Cunliffe Review’s call for asset health reform, helping water companies get ahead of the failure curve, even with limited data.

Partner quotes “This partnership could fundamentally change how the water industry approaches asset health. For the first time, predictive insight doesn’t require perfect data, and that’s a breakthrough the industry’s been waiting for.” Tom Ray, Director of Digital Products & Services (Digital Twins & AI), Binnies UK Ltd

"At Williams Grand Prix Technologies, we live by the same principles that fuel Formula 1– speed, precision, and relentless innovation. Scientific machine learning empowers us to predict complex system behaviours even from imperfect data, giving us the foresight that’s critical in high-stakes engineering. We’re thrilled to bring this capability to the UK water sector with Binnies and JuliaHub, transforming how asset health is monitored and helping the industry shift from reactive fixes to proactive resilience." Selin Tur, Managing Director & Chief Technology Officer, Williams Grand Prix Technologies  “SciML is revolutionising how critical industries such as aerospace, semiconductor, pharmaceutical and energy use AI. It thrives where traditional models fail, and we’re thrilled to see it applied to critical water infrastructure.” Deepak Vinchhi, Co-Founder and COO, JuliaHub, Inc.

Together, Binnies, Williams Grand Prix Technologies and JuliaHub are reshaping how the UK water industry approaches asset health. By combining high-performance AI from motorsport, precision engineering and deep sector expertise, they are introducing SciML for the first time. It’s a step change, enabling water companies to predict failures, optimise performance and invest with confidence, even with limited data. It’s a smarter, faster path to asset health.