Buildots Raises $130M as AI Data Center Boom Reshapes Construction

Buildots has raised $130 million in new funding as the construction technology company looks to expand its artificial intelligence platform across data centers, industrial facilities, and other large-scale infrastructure projects.

The round was led by O.G. Venture Partners, with participation from Lightspeed Venture Partners, Intel Capital, Mohari Ventures, Human Capital, Qumra Capital, Avigdor Willenz, Viola Growth, and Poalim Equity. It brings Buildots’ total funding to $297 million and follows what the company describes as a multi-year run of roughly 3x annual revenue growth.

The timing is significant. Construction is forecasted to be a roughly $16 trillion global industry by 2030, but a growing share of spending is being directed toward projects where delays are particularly costly: AI data centers, semiconductor facilities, advanced manufacturing plants, energy infrastructure, and other mission-critical developments. Buildots is betting that these projects will accelerate the construction sector’s shift away from manually collected progress reports toward continuously generated operational data.

Construction Becomes Part of the AI Infrastructure Race

Buildots is already deployed by more than 100 major construction firms and project owners, including Intel, Digital Realty, STO Building Group, JE Dunn, Mortenson, Bouygues, and HOCHTIEF. The company says seven-figure, multi-year agreements covering multiple projects are becoming increasingly common.

Data centers have become an especially important part of that expansion.

The enormous capital being directed toward AI infrastructure has created a second-order challenge: building the physical facilities required to run increasingly power-intensive computing systems quickly enough. Data center projects involve tightly sequenced electrical, mechanical, cooling, and structural work, meaning a relatively small delay in one trade can affect numerous downstream activities.

Buildots says its technology has now tracked and analyzed approximately 425 million square feet of construction, including data center projects representing 9.93 GW of delivered capacity and $88.3 billion in project value.

That gives the company an unusually large dataset for examining how construction schedules compare with what actually happens in the field.

How Buildots Uses AI to Understand a Construction Site

At the center of Buildots’ platform is a relatively straightforward idea: instead of asking project managers to manually determine how much work has been completed, use computer vision to observe the construction site directly.

Site conditions can be captured using 360-degree cameras, drones, and laser scans. Buildots then connects this visual information with the project’s Building Information Modeling (BIM) data and construction schedule. Its AI models determine which elements have been installed, where work is taking place, and how actual progress compares with the plan.

The company says site captures are typically processed into progress information and risk alerts within 24 to 36 hours.

This creates an evolving digital representation of the project rather than a collection of disconnected photographs and spreadsheets. Project teams can examine progress by trade, floor, area, activity, or individual construction element, including some work in areas where BIM data is incomplete.

The resulting dataset also feeds predictive systems.

Buildots’ Delay Forecast technology compares the planned pace of an activity with its observed pace and calculates the pace required to finish on schedule. The system can then flag activities that appear likely to miss their completion dates, allowing teams to examine scenarios such as adding labor, changing sequencing, or reallocating resources before a delay becomes critical.

That distinction is important. The longer-term opportunity is not simply automating construction reporting, but turning progress data into a forecasting system.

Data Centers Put Construction Schedules Under Pressure

Few areas illustrate that need better than data center construction.

AI infrastructure projects increasingly depend on complex mechanical, electrical, and plumbing work being completed within compressed schedules. A delay to electrical containment, cooling infrastructure, or another critical system can eventually affect commissioning and the date at which computing capacity begins generating revenue.

Buildots’ own analysis of 25 million square feet of global data center projects highlights the gap between construction schedules and real-world production rates.

Its 2025 data center benchmarking found HVAC work progressing at 76.9% of the pace required to meet planned targets. Electrical containment was running at 59.4%, while domestic water systems were progressing at just 44.9% of required pace.

Those figures are based on Buildots’ own project dataset, but they illustrate why owners of capital-intensive infrastructure are increasingly interested in more granular visibility into construction progress.

For a conventional project, a delay can increase financing costs and compress margins. For a hyperscale data center, it can also postpone the deployment of expensive computing infrastructure and delay when that capacity becomes operational.

Moving Beyond AI Progress Tracking

Buildots has also been expanding beyond its original computer-vision-driven progress monitoring system.

Its deviation detection capabilities compare construction against project models to identify incorrectly installed or unfinished elements. Teams can review the location and visual context around an issue before resolving it inside Buildots or exporting it to construction platforms including Procore, Forma, and Revizto.

Another feature, Completed Quantities, applies the same underlying site data to payment applications. Rather than relying solely on reported completion percentages from contractors and subcontractors, teams can compare payment claims against quantities the platform has verified as installed.

This pushes Buildots into an important but less obvious part of construction management: linking the physical state of a building to the financial state of the project.

The company is also moving further into workforce intelligence through Buildots Field, technology based on Genda, which Buildots acquired in October 2025. The system uses on-site Bluetooth infrastructure and mobile workflows to collect information on workforce presence, logistics, safety, and how labor is distributed across a construction site.

When combined with Buildots’ progress data, the goal is to connect labor inputs with construction output. A project manager could potentially see not only that an activity is falling behind schedule but also whether additional workers were actually deployed to that area and whether the intervention improved production. Buildots says the fully integrated Buildots Field application is scheduled for general availability in Q4 2026.

From Individual Projects to Construction Portfolios

The new capital will be used to expand Buildots in three directions.

Geographically, the company plans to increase deployments across major construction portfolios in North America and EMEA. Product development will extend the platform further across the construction lifecycle, from bidding through project delivery and handover. Buildots also wants to move higher into portfolio-level intelligence, allowing executives to compare performance across multiple projects rather than evaluating each development independently.

That could become increasingly important as companies simultaneously build networks of data centers, factories, energy facilities, and other standardized infrastructure.

Rather than treating every project as an isolated dataset, historical construction performance could eventually be used to establish benchmarks for future projects — identifying which trades consistently miss planned production rates, which schedule assumptions prove unrealistic, and which interventions actually recover lost time.

For Buildots, the $130 million round therefore represents more than an expansion of an AI construction monitoring product. The company is attempting to build a data layer connecting the construction site, project schedule, workforce, financial controls, and executive portfolio management.

As spending on AI infrastructure expands, the software used to manage the physical buildout is becoming part of the AI economy itself. Buildots’ challenge now will be demonstrating that the enormous volume of construction data it is collecting can consistently translate into fewer delays, lower costs, and more predictable project delivery at global scale.