Why nobody publishes a price per square foot
A 400,000 sq ft warehouse with clear sightlines and daytime access costs a fraction, per square foot, of a 40,000 sq ft process unit with congested pipe, escorted access, and a four-hour night window. Square footage alone predicts almost nothing.
What we can do is be transparent about the variables, so you can shape the scope toward the budget instead of discovering the mismatch after a proposal.
The seven variables that move the number
Every estimate we produce is a function of these, in roughly this order of impact:
- Area and geometric density — how many scan setups the space actually requires
- Access — escorts, permits, badging, night or weekend work, shutdown windows
- Control requirements — project-local grid versus surveyed state plane control
- Deliverable depth — point cloud only, 2D as-built drawings, or scan-to-BIM at LOD 200, 300, or 400
- Coverage — interior only, or interior plus roof and site with a drone flight
- Travel and mobilization — a real line item, and the reason a regional provider often prices better
- Deadline — compressed schedules mean larger field crews and parallel modeling
Where teams overspend
The most common source of avoidable cost is modeling everything at a uniform LOD. Most projects need one or two zones at fabrication-grade detail and the rest at LOD 200. A modeling matrix — which systems, in which areas, to which size threshold — usually takes more out of a price than negotiating a day rate.
The second is scanning a building twice because the first capture was scoped to a viewer rather than to a deliverable. If there is any chance the data will feed design later, capture at a density that supports it now; the field cost difference is small compared with a second mobilization.
How to get a useful estimate quickly
Send the building type, approximate area or acreage, the decision the data supports, access constraints, and the file format your downstream team works in. That is enough for a scoped range. Drawings help even when they are wrong — they tell us what changed.