Is There a Spreadsheet AI That Gives a Confidence Score for Each Row?

Yes. Several spreadsheet AI tools return a confidence score with each answer, so you can read only the uncertain rows instead of all of them. By the end of this guide you will know which tools do it, which one fits your list, and how to test any score on about 60 rows before you trust it.

Updated October 11, 2026.

Five tools put a confidence score next to the answer

They do not all mean the same thing by "score", and they differ in how much work it takes to use it.

With Clay you add and shape the confidence column yourself. With Columns you write the question as the header and the number comes with the answer. Neither is wrong. One is more setup, the other is less control.

Choose by what you already have: labels or just a question

Example: say a row has three fields scored 92, 88 and 41 percent. The row goes to review because of the 41. The rule is "lowest field score under my cut-off", and it takes one formula to apply.

Check six things before you trust a tool's score

Tick these before you pay for anything:

• There is a score on every row, not only on a summary.
• You can sort and filter by it.
• The scale is the same on every row.
• You can see which rows are low without opening each one.
• Answers come from a question you write, not a prompt you have to engineer.
• Pricing is by usage, so a small team is not charged per seat.

If a tool fails the first two, skip it. You would pay for a number and still read every row.

A stated confidence runs high on wrong answers, so test it on a sample

Having a score is not enough. Research on language models finds that verbalized confidence scores are often miscalibrated, with high confidence reported on answers that turn out to have low accuracy. A separate evaluation of how well models express their own uncertainty found they tend to be overconfident when they put it into words. A 95 means "start trusting" only after your sample agrees.

So treat the score as where to look first, then check it. This is the same sample habit as in how to know which AI answers in your spreadsheet are wrong, which is about checking answers you already have. This page is about picking a tool and testing its score.

The 60-row calibration test

Run your question on your own list. Sort by score. Pull 30 rows from the top band and 30 from the bottom band. Mark each right or wrong.

Example numbers, so you can see what a useful score looks like:

The gap between 97 and 47 percent says the score separates good rows from shaky ones. Now read the result:

• Top band clean, bottom band shaky: trust the high band and review only the low band.
• Both bands about equally right: the score is not helping on that question. Change the tool or the question.
• Errors in the top band: raise the cut-off or tighten the question, then test again. Do not just read more rows.
• Scores bunched near the top: the question is probably too vague. Rewrite it before you trust anything.

Your top-band error rate is your real miss rate. Three wrong in 30 is one in ten, and that is the number to plan around.

Set the cut-off on 30 rows, then review only what falls under it

Review load = rows x share below your cut-off.

Example: say a 5,000-row list and a cut-off that leaves 6 percent of rows under it. That is 300 rows for a human instead of 5,000. If you want the full walkthrough on a list that size, see how to qualify 5,000 leads without reading every website.

Then test the cut-off:

1. Read 30 rows just above it.
2. If more than 3 are wrong (one in ten), raise the cut-off.
3. If none are wrong, you can lower it.

Cost is the other number to check. Row-questions = rows x questions per row. Say 5,000 rows with 2 questions each is 10,000 row-questions, inside the Starter plan's 25,000 a month at $29. Pro is $99 for 150,000. Cached answers are not billed again, so re-running rows you did not change costs nothing extra.

What a confidence column looks like in a sheet

This is an invented example of one header and its two output columns:

You read the second row. You skip the first until your sample says the high band is clean.

Four mistakes that cost you the whole point of a score

• Comparing scores across tools as if 90 means the same thing. Scales and methods differ, so a cut-off from one tool does not carry over. Re-test it on 30 rows in each.
• Assuming a good prompt makes the score trustworthy. Clay users report that Claygent's confidence is influenced by prompt quality and structure, so a vague prompt can give a number that looks fine and means little.
• Reading only the low rows. You never learn whether the high rows are clean, so a hidden error rate stays hidden.
• Leaving the score in a column nobody filters on. You pay for the number and still read every row.

One more: test on a list that looks like your real one. A tidy sample gives a better-looking score than your messy export will.

Re-run only the rows under the cut-off

After you improve the question, re-run the low rows, not the whole list. Clay documents conditional runs for this, where a prompt runs only on rows with specific confidence values. In Columns, unchanged rows come from the cache.

Your next ten minutes

Export 100 rows from your own list with the columns the question needs, usually company name and website. Write one question as a column header. Run it, sort by confidence, read 30 rows from each end and fill in the table above. That uses 100 of the 500 free row-questions.

If the bottom band is clearly worse than the top, you have a score worth using. If it is not, you found that out for the price of ten minutes.

Try it on your own list

Columns answers a plain-English column question for every row and attaches a confidence number to each answer, and it never claims the answers are always right. The free plan gives you 500 row-questions with no card, enough to run the test above. Start free, no card.