How accurate is AI quoting, honestly

If a vendor gives you a single accuracy percentage with no context, be suspicious. Accuracy in estimating is not one number: it depends on what you feed the tool, what kind of job it is, and whether anyone checked the output. Here is a truthful account of where AI quoting is strong, where it drifts, and how to make it reliable enough to send.

Why there is no single accuracy number

Ask "how accurate is AI quoting" and you are really asking three questions at once. Did the software correctly understand the job? Did it produce sensible quantities from that understanding? And do the rates behind each line match what the work costs you? Each stage has its own errors, and they behave differently.

There is also an uncomfortable truth underneath the question: human estimates are not a gold standard either. Two experienced builders pricing the same extension routinely land meaningfully apart, and a builder pricing the same job twice, months apart, will not produce identical numbers. Estimating has natural spread. The fair question is not "is the AI perfect" but "is AI plus my review at least as good as what I do now, in a fraction of the time".

What actually drives accuracy up or down

From building and testing this technology, the biggest swings come from factors you partly control:

Notice that none of these is really about the AI model being clever. They are about the quality of what goes in and whether a professional looks at what comes out. This is the same physics as any estimating process, including a human one.

The errors that matter versus the ones that do not

Not all inaccuracy is equal. A quote that lands within a few percent of your hand-built number is a rounding difference; you would not have priced it identically yourself on a different day. The errors that matter are the big ones: a missed steel, a misread dimension that doubles a wall area, a hallucinated quantity, an entire trade absent from the bill.

These large errors are exactly the kind that structured output makes visible. When a quote arrives as a full bill of quantities rather than a single figure, a missing trade is conspicuous by its absence and a doubled wall area jumps out of the line items. A lump-sum guess hides its mistakes; an itemised bill exposes them. This is why the format of the output matters as much as the intelligence behind it.

How to make AI quoting safe to send

Accuracy is not something a tool has; it is something a workflow produces. The workflow that works looks like this:

1. Feed it properly. Give the tool the drawings plus a written description of anything the drawings do not show: existing conditions, client-supplied items, known complications.
2. Confirm the extraction. Check every number the AI pulled from your input before it prices. In QS Quoter this step is built in: extracted dimensions and specs are shown for confirmation, and nothing is priced until you approve them.
3. Scan the bill for shape. Read the bill of quantities top to bottom once. Is every trade present? Do the big quantities pass a sense check against the job in your head?
4. Check the two or three heaviest lines. Most of the money is in a few lines. Verify those against your own knowledge; the £40 lines can wait.
5. Correct rates as you go. Every rate you adjust makes the next quote start closer to your reality. Over months, the tool converges on how your firm actually prices.

That routine puts your professional judgement where it has the most leverage: checking scope, quantities, rates and risk rather than assembling the same document structure again. There is a longer discussion of this division of labour in human in the loop estimating.

The honest summary

AI quoting can assist an estimating workflow but is not accurate enough to run unsupervised. Fed decent input, on familiar domestic work and your own rates, it can produce a structured draft for professional review. That is not a magic robot estimator. It is a reviewable starting point whose scope, measurements, rates and assumptions still need a responsible human.