The artificial intelligence boom is creating an unprecedented demand for physical infrastructure. Data centres, power lines, and factories all require months of preliminary paperwork before construction can even begin. A British-founded startup called Build has emerged with a solution that slashes that initial grind by 95 percent using its own AI platform.
On Tuesday, Build announced it had raised $8.5 million (€7.4 million) in seed funding led by Index Ventures. Additional investors included Pebblebed, Puzzle Ventures, and Tiny.vc. The round also attracted a notable group of angel investors, including OpenAI's chief financial officer Sarah Friar and Blackstone's chief technology officer John Stecher, Build confirmed to Tech Funding News.
Build operates in the unglamorous but critical front end of construction. Before any data centre or power plant can be built, consultants must spend weeks on site sourcing, technical due diligence, power assessments, and environmental checks. This tedious process is both time-consuming and expensive, often delaying projects by months.
Build automates that work by pulling from more than 1,600 data sources and weighing planning, power, and political risks in parallel rather than sequentially. The company claims this approach cuts due-diligence timelines by more than 95 percent, turning what used to take weeks into mere hours.
Selling Work, Not Software
The twist in Build's business model is worth noting. Most AI startups sell software licences to enterprises. Build, however, sells the work itself on a monthly retainer, positioning itself as a replacement for consultants rather than a tool for them. This service-oriented approach allows the company to capture a larger share of the value it creates.
“We achieve software margins by automating much of our work, but our focus is on services spend, not software spend,” co-founder and chief executive James Stirrat-Ellis told Tech Funding News. A human still reviews each assessment before it goes out, ensuring quality and accountability.
This model is gaining traction in property technology. Paris-based Davis takes a similar route, handing clients finished feasibility studies rather than a subscription. Build's raise is part of a wider surge in construction AI investment. EU-Startups estimates roughly €112 million flowed into built-environment and construction AI across 2026.
Riding the Data Centre Gold Rush
The timing of Build's funding is strategic. The scramble to build AI capacity has turned land, power, and permits into the industry's tightest bottleneck. It is a multi-trillion-dollar infrastructure problem, and demand is outrunning what traditional developers can deliver. Hyperscalers are set to pour close to $700 billion into data centres by 2026, according to industry projections.
New sites are rising fast, from Europe's largest buildouts to Blackstone's first AI-era data-centre REIT. Every one of them needs the early groundwork that Build sells. The startup has already run more than 100 projects across 15 countries for clients including property giant Tishman Speyer, and it recently landed its first hyperscaler customer. Stirrat-Ellis called that “the top customer you can get” in data-centre work.
The company remains tiny, with around 10 staff spread across four continents, but it plans to hire more as demand grows. The lean team reflects the efficiency of its AI-driven approach, which automates the majority of the work while humans handle quality control.
An Architect Who Learned to Code
The idea for Build came from personal frustration. Stirrat-Ellis trained as an architect and worked on the S$13 billion expansion of Singapore's Changi Airport. During that project, he witnessed how lengthy consultant reports and scattered data dragged out timelines unnecessarily. Determined to find a better way, he left a master's programme at Harvard, taught himself to code, and founded Build in 2024 alongside AI researcher Ben McClusky.
Index Ventures, fresh from deals such as its $60 million bet on Conduct, moved quickly on the opportunity. “The round came to us. We got a term sheet within a day,” Stirrat-Ellis said. The rapid decision underscores investor confidence in Build's potential to disrupt a long-standing inefficiency in the construction industry.
Whether Build can hold its lead is the open question. Big consultancies such as CBRE, Arup, and JLL are adding AI capabilities of their own. Build's wager is that replacing them beats helping them, and that the firm which maps the ground fastest wins the race to build. With its unique service model and growing customer base, Build appears well-positioned to capitalise on the infrastructure demands of the AI era.
The data centre construction boom shows no signs of slowing. According to industry analysts, global data centre capacity is expected to double by 2030, driven by cloud computing, streaming, and AI workloads. This will require massive investments in land, power, and cooling infrastructure. Build's ability to compress the due diligence phase from weeks to hours could become a competitive advantage for developers racing to secure prime sites.
Environmental considerations are also becoming more critical. Data centres consume enormous amounts of electricity and water, raising concerns about sustainability. Build's platform incorporates environmental checks into its assessments, helping clients navigate regulatory requirements and public scrutiny. This holistic approach addresses both planning and power risks alongside political risks, providing a comprehensive view of project feasibility.
The startup's technology relies on a combination of natural language processing, machine learning, and data aggregation. By pulling from thousands of public and private databases, Build can quickly identify potential issues such as zoning restrictions, grid capacity constraints, or community opposition. The parallel processing of these factors marks a departure from traditional sequential analysis, which often missed interdependencies.
Stirrat-Ellis's background as an architect gives him firsthand insight into the pain points of large-scale construction projects. He experienced the frustration of waiting for reports that could have been automated. His decision to teach himself coding reflects a broader trend of domain experts entering tech to solve industry-specific problems. This combination of deep industry knowledge and technical skill is rare and valuable.
Ben McClusky, the AI researcher who co-founded Build with Stirrat-Ellis, brings expertise in machine learning and data science. Together, they have built a platform that not only speeds up due diligence but also improves accuracy by reducing human error. The human review step ensures that edge cases and nuanced local regulations are properly addressed.
Looking ahead, Build plans to expand its data sources and refine its AI models. The company aims to cover more regions and types of infrastructure projects beyond data centres, such as renewable energy plants and manufacturing facilities. The seed funding will be used to hire engineering and commercial talent, accelerate product development, and scale customer acquisition.
The construction industry has been slow to adopt digital transformation, but the pressure to build faster is forcing change. Build's success will depend on its ability to build trust with developers and consultants who have relied on traditional methods for decades. Early wins with Tishman Speyer and a hyperscaler suggest that the market is ready for a better way.
As AI continues to reshape industries, the physical infrastructure that supports it must keep pace. Build is betting that its AI-driven paperwork automation will be the key to unlocking faster, smarter construction. The $8.5 million seed round is a vote of confidence in that vision, but the real test will be whether Build can scale its model and fend off competition from established players. For now, the startup is focused on execution, working to turn the data centre gold rush into a sustainable business.