Buildots
AI that compares hard-hat camera walks against the BIM model to measure what's actually built.
Quick Answer
Buildots is an AI-based construction progress-tracking platform. A site engineer wears a 360-degree camera on a hard hat and walks the project on a fixed cadence; computer vision compares the imagery to the BIM model and schedule to measure installed work at the element level, flag deviations, and forecast delays — without manual data entry.
The Full Picture
Progress tracking has traditionally relied on a superintendent's walkthrough and a percent-complete estimate reported up the chain — useful for a status meeting, but subjective and hard to verify against what the schedule actually planned. Buildots exists to replace that judgment call with a measured comparison: what's physically installed, checked against what the model and schedule say should be installed, by when.
Mechanically, a site engineer clips a 360-degree camera to a hard hat and walks the site on a scheduled cadence, typically weekly. The imagery uploads to the cloud, where computer vision locates each capture within the building, identifies installed elements, and compares them against the BIM model and the project schedule. The output is element-level progress data, deviation flags, and delay forecasts, plus a generative AI assistant for querying status — all without anyone manually logging what was seen.
In practice, a team building a large industrial or data center facility uses the weekly walk to catch drift early: a mechanical room running behind its planned date shows up in the dashboard the same week it happens, not weeks later when it becomes a visible schedule problem. Buildots reports meaningful reductions in project delays for clients including Intel and dozens of construction firms using the platform across multiple countries (Buildots, 2026).
The platform's value depends on adoption discipline. It only works if crews complete the walk on schedule and the underlying BIM model stays current enough to compare against. A skipped walk or a stale model turns a quantitative progress tool back into the same subjective status update it was built to replace — the technology doesn't remove the need for process discipline on site.
Real Examples
Common Misconceptions
People assume: People assume Buildots is just a photo archive of the job site.
Actually: it's computer vision that measures installed quantities element by element against the BIM model and schedule, producing quantitative percent-complete data — not just a searchable photo log.
People assume: People assume AI progress tracking replaces the superintendent's judgment.
Actually: it gives the superintendent better data to act on faster, showing precisely where actual installation is falling behind plan. Deciding how to recover the schedule is still a field-management call the tool doesn't make.
Frequently Asked Questions
What is Buildots used for?
Buildots is used by general contractors and project owners to track construction progress objectively. It compares hard-hat camera captures against the BIM model and schedule to produce element-level percent-complete data, flag deviations, and forecast delays before they become visible schedule problems.
How does Buildots capture site data?
A site engineer wears a small 360-degree camera on a hard hat and walks the project on a set cadence, usually weekly. The captured imagery uploads automatically, and Buildots' computer vision locates and analyzes it against the BIM model without anyone manually entering data.
Who uses Buildots?
General contractors and owners running large, schedule-sensitive builds — data centers, life sciences facilities, and industrial projects among them — use Buildots to keep progress data objective and current across complex, fast-moving job sites.
What's the difference between Buildots and OpenSpace?
Both use hard-hat 360-degree cameras to document sites, but the emphasis differs. OpenSpace is built around navigable photo documentation and visual site records; Buildots is built around comparing captures to the BIM model and schedule to produce quantitative progress and delay-forecast data at the element level.