We Tested Togal.AI on a Real Bid Set — Here's Where It Sped Us Up and Where We Still Hand-Checked It
Vendor demos are built to look good. The only way to know whether an AI takeoff tool actually holds up is to run it against a real, messy bid set — the kind with revision clouds, inconsistent schedules, and details buried three sheets away from where you'd expect them. So that's what we did, running a live commercial project through Togal.AI alongside our normal manual takeoff process to compare results trade by trade.
The Project We Used
We ran the test against a mixed-use commercial set — a project with a straightforward shell and core, but real-world messiness in the finish schedules and MEP coordination, the kind of set that stress-tests a tool more honestly than a clean demo file would.
Where It Sped Things Up Meaningfully
Door and window counts Doors & Windows Estimating were the clearest win. The tool correctly identified and counted the overwhelming majority of openings across the floor plans in a fraction of the time a manual count would take, and flagging discrepancies against the door schedule was straightforward once we cross-referenced its output. Room-level flooring and finish quantities Interior & Exterior Finishes came out similarly strong — square footage by room, pulled automatically, matched our manual verification closely enough that we'd trust it as a first-pass number on future projects.
Sitework parking and paving area Sitework & Civil also performed well, since these are large, geometrically simple areas that don't require interpreting written notes to quantify correctly.
Where We Still Hand-Checked Everything
Reinforcing was the biggest gap. The tool doesn't reliably interpret grout fill schedules or reinforcing patterns called out in structural notes Masonry Estimating rather than shown graphically, which matches what we'd expect from a computer-vision-first tool — it sees geometry, not embedded engineering logic. We didn't trust any reinforcing-related output without a full manual review.
Structural steel connections [Structural Steel Takeoff](Columns & Beams — /trades/structural-steel/structural-steel-takeoff-columns-beams/) were a similar story — tonnage and piece counts came out reasonably close, but connection type classification, which drives a large share of fabrication cost, needed a person who actually understands moment connections versus simple shear connections to correct.
Our Verdict After the Test
For this project, AI-assisted detection cut meaningful time off the visually mechanical scopes — doors, windows, finishes, paving — without a meaningful accuracy tradeoff once we spot-checked the output. For anything structural or reinforcing-related, it functioned as a rough first pass at best, and we built our actual bid numbers off a full manual takeoff for those scopes regardless. The honest conclusion: it's a genuine productivity tool for the right subset of a takeoff, not a wholesale replacement for an estimator who knows the trades.
<OurProcess /> <CTASection />Related Questions
Is Togal.AI reliable for structural takeoffs? Based on our test, not without significant manual review — reinforcing and connection-type details require interpreting structural notes the tool doesn't fully capture.
How much time did AI-assisted takeoff actually save? The biggest time savings showed up on geometrically simple, visually countable scopes like doors, windows, and finishes — the mechanical counting work that historically took the longest for the least judgment.
Would you recommend AI takeoff tools for a full bid? As a first-pass tool for the right scopes, yes. As a full replacement for a manual takeoff on structural or code-driven trades, not yet.