Where the 40 percent comes from
The 40 percent in the title is my modeling assumption, not a measurement. When I ask contractors on the program how much faster estimating got, the answers come back between 30 and 50 percent. I personally think a dialed-in program is easily over 50 — but the project mix drives that, which is exactly why this page is a model with a calculator instead of a promise.
So treat every figure below as the worked example it is, then run the calculator with your own inputs.
The baseline scenario
Start with a company that produces $10 million in estimated volume a year and closes 25 percent of it. That is $2.5 million in awarded revenue coming out of the estimating department annually.
Nothing exotic — just a number to anchor the math. Your volume and close rate go in the calculator.
What a speed gain does to capacity
A 40 percent increase in estimating throughput raises estimating capacity from $10 million to $14 million. If the close rate holds, awarded revenue can move from $2.5 million toward $3.5 million — when the pipeline actually supports the extra capacity.
That last clause is doing a lot of work, which is why the model does not stop here.
The honest variable: pipeline utilization
Extra estimating capacity only pays when there is work to fill it. The model handles that with a pipeline utilization factor — how much of the new capacity your market and sales effort can actually feed, from zero to one hundred percent.
At 50 percent utilization, the example company sees about $500,000 in incremental awarded revenue. At full utilization, about $1 million. Use a conservative utilization figure to stress-test the whole idea against your own backlog.
ROI and break-even
Assume a $25,000 all-in investment to achieve the gain — drone, licensing, program, and time — and 20 percent gross margin on incremental work. The example scenarios run from roughly 100 percent ROI at a quarter utilization to roughly 700 percent at full capacity.
Break-even comes surprisingly early: at 30 percent margins the example breaks even with just over 8 percent of the new capacity filled; even at thin 10 percent margins, one quarter of the new capacity covers the investment.
What protects the model
The speed has to come from better workflow and better roof information — not from cutting corners on the estimate. The model assumes estimate quality holds and the close rate stays constant.
That assumption is the reason quality control exists in the program: every capture gets checked before anything is hosted, so faster never quietly becomes sloppier.
Run your own inputs
Defaults match the example above. Change anything.
The model assumes estimate quality is maintained and the close rate stays constant. Use conservative pipeline utilization to stress-test.
