In our latest Spotlight Interview, we spoke to Nick Tune, CEO at Optimise AI. Nick shares how the company grew from nearly two decades of research to tackle rising energy costs and net zero goals. He talks about how their Predict tool works with limited data and the use of digital twins and real-time analytics to create actionable strategies for asset managers. “We’ve developed ways to extract maximum value from this limited data by combining smart meter information with building physics models and machine learning across large datasets, comparing how similar buildings perform. What sets us apart is our 18 years of R&D experience and our development of semantic data structuring – essentially creating perfectly formed small language models trained on high-quality, peer-reviewed sources rather than scraping the entire internet like large language models do. This approach is more efficient and delivers more accurate results.”

Q: Can you tell us what inspired the founding of OptimiseAI and what challenges in the industry are you aiming to address?

The energy shock from Russia’s invasion of Ukraine and the urgent drive for decarbonisation created a perfect storm that convinced me we needed to act. We were sitting on a goldmine of knowledge and technology developed over 18 years of research at Cardiff University, and we could see how desperately the built environment sector needed help managing soaring energy bills while pursuing net zero targets.

We established our research centre in 2007, pioneering the use of Building Information Modelling (BIM) to reduce energy consumption in buildings – long before BIM became mainstream in the UK. We evolved through machine learning to what’s now called digital twins, always staying ahead of the curve in using data to manage buildings more effectively.

The problem we’re solving is stark: only 10% of non-domestic buildings globally have Building Management Systems, and of those, perhaps one in several thousand actually exploit their data effectively. That leaves 99% of buildings running inefficiently, with owners and operators having no real insight into their energy consumption patterns.

Q: Could you explain how OptimiseAI’s Predict tool delivers instant decarbonisation guidance, even when building data is sparse?

Our Predict tool was specifically designed for this data scarcity. While most buildings lack sophisticated monitoring, about 60% now have smart meters that few organisations use effectively. We’ve developed ways to extract maximum value from this limited data by combining smart meter information with building physics models and machine learning across large datasets, comparing how similar buildings perform.

What sets us apart is our 18 years of R&D experience and our development of semantic data structuring – essentially creating perfectly formed small language models trained on high-quality, peer-reviewed sources rather than scraping the entire internet like large language models do. This approach is more efficient and delivers more accurate results.

Q: How does OptimiseAI support asset managers and sustainability teams in transforming fragmented data into actionable strategies?

For asset managers and sustainability teams, we’re transforming their roles from data collectors and report writers to innovation drivers delivering tangible business value. Take our work with ScotRail across 25 stations and depots – we’re not just helping them plan their net zero journey, but providing granular, real-time optimisation that lets them measure actual energy savings from specific interventions.

Q: How are digital twins and real-time analytics contributing to enhanced asset value and decarbonisation clarity?

A good example is our ongoing project with Connected Places Catapult and a freight company, based in Cambridgeshire. They face the classic challenge of having abundant data from their buildings, sub-metres, and vehicle fleet, but struggle to extract meaningful value from these siloed information sources.

They’re expanding rapidly but face severe grid constraints – a common problem for growing businesses investing in electric vehicles. With plans to scale from two electric vehicles to 200, whilst managing large photovoltaic arrays that aren’t being efficiently utilised, they need sophisticated load balancing.

Our solution enables them to optimise renewable energy production, minimise overall energy consumption, and intelligently balance supply and demand based on real-time availability and operational needs.

Q: Looking ahead, what does success look like for OptimiseAI in the long term and are there any exciting future plans you would like to share?

The exciting future lies in expanding beyond energy. We’re seeing huge appetite for applying our technology to water management and air quality optimisation. With 30% of all water consumption coming from non-domestic buildings, and just 1% of businesses consuming half of all commercial water, there’s enormous potential for impact.

Success for OptimiseAI means helping organizations tackle their most complex sustainability challenges – from airports and train stations to logistics depots. We’re looking for the complicated cases with disparate data sources, because that’s where our technology truly shines. Whether it’s energy, water, or air quality, we’re ready to help turn fragmented data into actionable strategies that deliver real environmental and financial returns.