PlanProof vs Togal.AI: an honest comparison

What each tool is built for

Togal.AI publishes a per-seat price of $299 per user per month and positions itself around measurement speed: AI that automatically detects and measures spaces and features across a plan set, so an estimator spends minutes where clicking through sheets took hours. That is the published pitch, and it addresses a real cost — measurement time is where most estimating hours go.

PlanProof is built for residential plan sets and is organized around a different output: not just quantities, but the evidence behind them. Every line on the material list carries a proof status — proved (traced to drawn geometry at a proved scale), bounded (held between stated limits the evidence supports), flagged (a quantity that needs review, with the reason), or refused (no number, with the precise reason one could not be measured). Before any status is published, an adversarial proof audit attacks each number, looking for the contradiction, scale problem, or missing evidence that would demote it.

These are different jobs, not two prices for the same product. One optimizes the time it takes to get numbers; the other optimizes what you can do with the numbers afterward — trace them, defend them, and know exactly which ones still need a human look.

Pricing, side by side

Togal.AI publishes $299 per user per month — about $3,588 a year per estimator, flat, regardless of how much you bid. PlanProof prices by the work: $299–$499 per single project, $199–$299 one-time for owner-builders, and monthly builder plans at $499–$2,499/mo scaled by project volume.

• Bidding in bursts — a builder running four or five plan sets a year pays roughly $1,200–$2,500 per-project against $3,588 for a seat, and pays nothing in the months between.
• Steady volume — around ten projects a year the totals converge ($2,990–$4,990 per-project against $3,588 per seat), and a monthly plan starts to make sense; from there, compare plan tiers against seats times estimators.
• A full-time estimator measuring daily — this is where per-seat pricing is at its best: one flat number, easy to budget, and a busy seat earns it back.

Prices are as published at the time of writing; check both vendors’ current pages before deciding on cost.

The evidence question

Here is what PlanProof publishes about its own output. Every quantity it proves is traced to a spot on the drawings — tap the number and an overlay shows where it was measured, at what scale. Every line carries a published proof status, and an adversarial audit runs before any status ships. What it cannot prove, it refuses, with the precise reason. Exports are signed, so the trace survives outside the app.

Togal.AI does not publish equivalents — per-line proof status, evidence trails, or refusal behavior. That is not an accusation; it is the boundary of what can honestly be compared. We can compare what each company publishes, and on this axis the comparison is short: PlanProof publishes its evidence model, and Togal.AI does not publish one.

For any tool that does not publish this — including any future tool you evaluate — the demo is where you find out. Pick a quantity and ask to see where it was measured. Ask what appears when a sheet cannot be measured. Ask what the export records about where each number came from. The answers are the evidence model, published or not.

Where Togal.AI is a strong choice

An honest comparison names the cases that favor the other tool. A fast per-seat measurement tool at a published $299 per user per month is a reasonable call when:

• The seat is busy — a full-time estimator measuring across plan sets all month gets full value from flat per-seat pricing, and the budgeting is simple.
• Measurement speed is your bottleneck — if your team already has a review and QA process it trusts, a tool that compresses the measuring step slots into the workflow you have.
• Your work runs beyond residential — PlanProof is built for residential plan sets. If your book is broader than that, evaluate with that in mind.

We do not publish claims about Togal.AI’s accuracy or internals, and nothing here should be read as one. Whatever tools make your shortlist, run your own plan sets through them — that is the only benchmark that reflects your work.

Where PlanProof differs

The difference is a design commitment: what PlanProof can prove, it traces to the drawings; what it cannot prove, it refuses with a reason. In between sit bid-safety flags — quantities that carry a warning and need a human look before they carry dollars. Flags are inputs to your review, not a replacement for it.

The same evidence substrate powers the rest of the product. Plan-health reporting tells you what a set can and cannot support before you spend hours on it. Revision compare shows what actually changed between plan issues, line by line. Quote checking puts a supplier’s numbers against the proved quantities. Each of these exists because the evidence behind every line is stored, not discarded after the takeoff runs.

And because exports are signed, the defense travels: a supplier, customer, or lender holding only the file can verify what was measured, what was flagged, and what was refused. When a quantity is disputed in October, the takeoff from March can still answer for itself.

How we test our own numbers

Every statistic we publish comes from one place, with one framing: in our internal gauntlet across 33 real public plan sets, 85% of plan sets were takeoff-ready, 97% of critical lines were proved or bounded, 0 critical lines were false-proved, 0 do-not-bid lines shipped, and runs took 2s–74s per plan set. The remainder surfaced as flags or stated refusals rather than silent guesses — which is the designed behavior, not a shortfall being spun.

The framing is the point. These are internal results on public fixture sets, not an independent benchmark, and they are not a comparison to Togal.AI — Togal.AI publishes no equivalent numbers, so no like-for-like comparison exists, and we will not invent one. What the gauntlet tells you is how we hold ourselves to account; what your own plan sets tell you is whether that holds for your work.

Who should pick which

• Pick Togal.AI if you keep a full-time estimator measuring across plan sets all month, per-seat budgeting suits you, and your existing review process is the trust layer for the numbers.
• Pick PlanProof if you build residential, bid in bursts where per-project pricing fits, and your bids need quantities you can trace and defend line by line — with flags and refusals telling you exactly where review time should go.
• Either way — run a real plan set from your own backlog through whatever you evaluate, including one messy set, and ask the evidence questions above. Behavior on a bad set in a demo predicts behavior on bid week.

The honest summary: Togal.AI publishes measurement speed and a seat price; PlanProof publishes its evidence model, its refusal behavior, and its own test results. Weigh what each company is willing to put in writing.

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