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Kanopi: an accuracy claim that survives being checked

Principal, LÏEF Development; the work ran through Kanopi, LÏEF's estimating system; 2026 to present

IndustryConstruction and real estate development
Project typeApplied AI system build and validation
Dates2026 to present
SeatPrincipal, LÏEF Development, and the estimating seat
EntityKanopi, LÏEF Development's estimating system

Nobody. I built Kanopi for LÏEF's own bids because outside takeoff vendors were slow and rate sheets were scattered, a problem I had first solved at Tellus in 2014.

By April 2026 the pipeline priced a plan set in minutes; nobody could say whether to trust it.

Problem

A test that separated memory from forecast, a calibration pass against real bids, a takeoff measured from the drawing's own geometry, and the discipline to publish the unflattering result.

What we did

In-sample numbers are never an accuracy claim. Only the leave-one-out figure ships, with its ground truth and corpus size beside it.

Result

Held out one bid at a time and priced blind, mean error 18.6 percent across three single-family bids, worst bid 31.9 percent (June 10, 2026), its only accuracy claim. As of September 25, 2026, in place with eight builders and developers; over $600 million in proposals bid nationally in 2026.

A model graded on the jobs it was tuned on is grading its own homework.Jesse Fowler

Read on

01

The decision and the reasoning

Why we did it this way, told first.

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02

What we did and what it produced

The work, decision by decision, and the result.

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03

A slice of the project list

A few related projects.

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Team

Private side

The full report, the source files on record, the timeline and every figure with its source. Password.

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