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Receipt · Shared by the builderReceipt no. 2275
What sucked

A commercial general contractor and construction company starts every bid with a manual takeoff.

Estimators pull equipment and material counts off the drawings in Bluebeam, convert the measurements using SMACNA standards, wait about a week for vendor pricing, and then build the bid in Excel. The process is slow, and it is the biggest drag on getting bids out the door. The bigger issue is accuracy. Different estimators produce materially different labor-hour numbers for the same job, roughly a 10% spread, which on a $1M project means $50K to $100K of exposure. Bluebeam also won’t flag what nobody counted. A missed exhaust fan or other piece of equipment doesn’t show up until after the bid is won, and by then it comes straight out of profit. Errors were mostly caught late, at the submittal stage, with no real checks and balances up front.

Operations4–8 weeks
What they built

We built an AI-assisted takeoff and estimating workflow that sits alongside the company’s existing process. When a bid package comes in, the estimator uploads the drawings and specs. The system reads them and produces a complete equipment checklist with counts and the relevant spec details, so nothing in the package gets overlooked. The estimator reviews and approves that list, so the automation handles the counting while the judgment stays with their team. The approved list is then automatically checked against the Bluebeam takeoff, and any discrepancy gets flagged before the bid goes out. That catches the missed-equipment problem at the source. Labor hours and material counts are generated from the company’s own historical estimates, so every estimator works from the same baseline and the variance between them shrinks. Vendor pricing requests go out and come back through a single interface instead of a week of email back-and-forth, and the results feed straight into the bid sheet. The final output is a draft bid in the Excel format the team already uses, ready for a final estimator review.

Time to results
4–8 weeks
Project cost
$25K – $100K
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Who did it
Martijn Lancee
Eclipse AI