AI SEO Tools: How to Tell Which Numbers a Model Produced

An AI SEO tool is SEO software that hands part of the job to a model, whether that job is scoring a draft, estimating keyword demand, explaining an audit or writing a meta description. How much AI a tool advertises tells you little; what separates one from another is where the model sits relative to the figures you act on. For an in-house SEO or growth lead choosing a tool, or auditing the one already on the invoice, what follows is a five-rung scale for where a number comes from, a check you can run on your own account, and six tools read through their own documentation. GrowthHasten makes one of the six; its entry uses the same four labels as the other five.

The short version

• Sort tools by where each figure comes from, not by whether the product page says "AI". The test applies to long-established suites as much as to products sold as AI.
• Semrush and Ahrefs both describe their search volume as the product of a model, in their own help documentation. That makes it good for comparing keywords and weak as a traffic forecast.
• Before trusting a tool's traffic figure for a page, set it beside Google Search Console's clicks for the same page and dates.
• Ask whether the tool can change your live site, and what a person approves first. Google says review of AI output covers titles, meta descriptions, structured data and image alt text.

What is an AI SEO tool, and what kinds are there?

Any SEO product in which a model does part of the work. Four job types matter here: content scoring and drafting, where a model compares a draft with ranking pages; keyword and traffic data, where it turns sampled inputs into estimates; site auditing and fixing, where it explains findings, drafts metadata and in some products deploys the change; and general assistants such as ChatGPT and Claude.

A fifth type, software that checks whether AI answers mention your brand, answers a different question. Our comparison of trackers that monitor a brand inside AI answers covers it, and topic research tools are left out for the same reason.

Which numbers in an SEO tool did a model produce?

More of them than the label suggests. We sort them into five origins, listed here from easiest to hardest to check.

Where the figure comes from	A named example	What to do with it
Measured from a first-party source	Search Console clicks and impressions, which Google describes as data "directly from Google Search itself"	Treat it as the record for your own site
Computed from measured figures	Click-through rate; a count of broken links found in a crawl	Trust the arithmetic, then check the inputs and how many pages were read
Estimated by a model the vendor documents	Semrush search volume; Ahrefs search volume and organic traffic	Rank candidates against each other. Do not forecast with it
Benchmarked against model output	Surfer's AI Search Score, whose Facts Coverage compares a draft with facts "commonly included in AI-generated responses for the topic"	Read it as a coverage signal, not a fact check
Generated by a language model	A figure an assistant types with no data source attached to the conversation	Treat it as a placeholder until you find its source

On the bottom rung, Google's guidance on using generative AI content is direct: generative models "don't retrieve facts, but predict a likely sequence of words based on their training data." A search volume typed by a chatbot with nothing to read from is a sentence that looks like a number.

The middle rungs are where readers err the other way. An estimate is not a fault. Ahrefs's help center says marketers use its traffic data to compare websites and to see whether a site is growing or losing traffic, which is a job a documented estimate does well. Trouble starts when an estimated column is read as a prediction of what a new page will earn; we cover working with modeled volume and difficulty columns in our gap analysis guide.

A tool can do this labeling for you. GrowthHasten's reports tag each figure Measured, Computed, AI-written or Not measured, and our piece on SEO scores explains how a provenance label should change a decision.

One limit. A new site with no Search Console history has nothing measured to anchor against, so for its first months it has to lean on estimates for demand. Use them, and write "estimate" next to each one.

How do you check where a tool's numbers come from?

Trace the few figures you actually act on, one at a time.

1. Pick three figures: the ones that change what your team does next, such as a page's traffic, a keyword's volume and a site health score.
2. Find the methodology page: the help center article that explains how each figure is produced. Feature pages describe outcomes; methodology pages describe inputs.
3. Compare one figure with Search Console: set the tool's traffic figure for one page and one month beside Search Console clicks for the same page and range. The gap is not a verdict on the tool. It tells you how much weight that estimate can carry on your site.
4. Repeat any model-written request: ask for the same keyword cluster or title twice and compare. Whatever changes between runs was generated, not read from data.
5. Ask the vendor: use the questions below.

Question to put to the vendor	An answer worth trusting	A warning sign
Which figures can a model change?	A specific list, with numbers excluded or labeled	A general answer that the AI handles everything
What appears when a data source is missing?	A blank, or an explicit "not measured" label	A zero, or an estimate in the same column as measured data
Can each AI-written recommendation be traced to a finding?	Every recommendation points to the check or data row behind it	Advice with no visible source
Does the tool write to the site, and who approves it?	A stated default, an approval mode and a rollback	Automatic changes with no log

Search Console is the reference in step 3 for a plain reason. Google's page on third-party SEO tools and services says such tools "don't have access to our internal ranking data," and that "Any predictions are their own."

Which AI SEO tools are worth a look, and where does each model sit?

Six tools, one or two per job type, chosen to illustrate the scale rather than to rank a market. Each entry is a reading of the vendor's own documentation as published on October 2, 2026, not a hands-on test. Prices are out of scope; check each vendor's pricing page. GrowthHasten is our product, and it gets the same four labels as the rest.

Tool	Where the model sits	Can it change your live site?
Surfer	Scoring a draft against ranking pages and AI answers	Not addressed in the docs read
Semrush	Producing search volume from a computational model	Not addressed in the docs read
Ahrefs	A volume model, with a click-through model on top for traffic	Not addressed in the docs read
Search Atlas OTTO	Finding fixes and writing them, content included	Yes, by default, with an optional approval mode
GrowthHasten	Explaining findings its checks verified, and drafting titles, meta descriptions and alt text	No
ChatGPT or Claude	Everywhere, because the assistant is the model	Depends on what a connection allows

Surfer

The job: scoring a draft in Surfer's Content Editor as it is written.

Where the model sits: in the benchmark. Surfer's help article on Content Score describes a 0 to 100 score built from an SEO Score and an AI Search Score, comparing the draft with "the top 20 pages ranking for your target query". The AI Search Score's Facts Coverage measures the draft against what AI-generated answers on the topic usually include, so one input is other models' output.

What to check: which screen the number came from. Content Editor shows all three scores, while Audit, Content Audit and SERP Analyzer show SEO Score only, and Audit reads the whole page body, navigation and footer included, so it can score the same content differently. Compare like with like.

Write access: the two Content Score articles read do not cover publishing, so this entry makes no claim either way.

Semrush

The job: keyword research inside a full SEO suite.

Where the model sits: in the search volume itself. Semrush's knowledge base article on search volume says it uses "machine learning algorithms and petabytes of accumulated data", overlays third-party data with clickstream data, and "receives a computational model" that it applies to billions of keywords.

What to check: the averaging. Volumes update monthly and the figure is the past 12 months divided by 12, so a seasonal keyword's average can sit far from any single month. It is not split by device. Semrush also lists an MCP connection for AI assistants in its site navigation; what it can do was outside the documentation read.

Write access: the volume documentation does not cover site changes.

Ahrefs

The job: keyword and organic traffic research on your domain and competitors'.

Where the model sits: in two layers. The Ahrefs help center article on why its traffic differs from Google Analytics and Search Console describes a volume model combining Google Keyword Planner data with clickstream data, then a CTR model that estimates clicks from volume and position.

What to check: organic traffic, which Ahrefs calls "just an estimate" that cannot stand in for a site's actual search traffic. Use it for comparisons and trends, and take your own clicks from Search Console.

Write access: the help article read covers data only.

Search Atlas OTTO

The job: on-page fixes and new content, delivered through a pixel in the site header.

Where the model sits: in finding the change and making it. Search Atlas's OTTO Pixel page says OTTO "writes and ships meta tags across every page" and "writes and publishes original content straight to your site on its own".

What to check: a sample of changed pages after each deployment, since the output is live copy rather than a report.

Write access: yes, by default. The page states "By default, OTTO SEO deploys fixes on its own," with an approval mode "to review every change before it goes live, and roll back anything, anytime." That suits a team that wants fixes shipped without a queue. Make the mode a decision, not a default.

GrowthHasten

The job: auditing your own site and joining what the crawl finds to your Search Console and GA4 figures, both optional connections.

Where the model sits: after the checks, never inside them. More than 120 deterministic checks produce the findings, and only then is a model handed them to explain and put in order. In the product page's words, it "has nowhere to put a number, and any recommendation pointing at an issue we did not actually find is discarded before it reaches you." The Opportunities section lists 0 numbers AI may edit, and a topic map that came from the model rather than your own search data is labeled that way above the results.

What to check: the AI-written titles, meta descriptions and alt text the audit drafts as suggestions. If the model step fails, each insight keeps its detector's own sentences and the screen says why. The limits: no rank tracking; no search volume, keyword difficulty or CPC; a ceiling of 5,000 pages in each crawl; and a single crawl per site each day. Free, with an account.

Write access: none. Writing to your website is on the page's list of things the product does not do, and its assistant connection is read-only.

ChatGPT and Claude, used directly

The job: whatever you ask, from explaining a report to drafting a title.

Where the model sits: everywhere. A figure given with no data in the conversation is predicted text.

What to check: the source of every number. If it is not a file, page or connected tool in that conversation, treat the figure as unverified.

Write access: set by the connection, which the section on assistants below covers.

Should an AI SEO tool be allowed to change your site?

Only with a person approving what a model wrote before it ships. For most in-house teams, start there and loosen it deliberately. Google's generative AI guidance points the same way: the review it asks for "also applies to metadata like <title> elements, meta description elements, structured data, and alternate texts for images," and meta tags are exactly what OTTO's page says it writes.

The two write-relevant designs above sit at opposite ends. OTTO deploys by default and lets you switch on approval with rollback. GrowthHasten never deploys; every proposed internal link is, in its own wording, "a candidate for a person to judge." Our recommendation sits between the two: changes queued, a named owner approving them, and a log of what went live.

When not to insist on approval: a large site with templated, low-risk fixes, such as missing alt text across thousands of product images, can reasonably accept automatic deployment if rollback works and someone samples the output each week. Reversibility is the deciding test. For which SEO tasks to hand an agent at all, see our guide to deciding what an agent may do on your site.

Can ChatGPT or Claude replace an AI SEO tool?

For explaining, grouping and drafting, often. For figures, only when connected to a data source and asked which number came from where.

The test fits in one question: where did that number come from? If the assistant cannot point to something in the conversation, the number is a prediction, however precise it looks.

The Model Context Protocol (MCP) changes that. It lets an assistant read a tool's data instead of predicting it, and Semrush and GrowthHasten both offer a connection. The question for any MCP server, ours included, is what it lets the assistant change. GrowthHasten's exposes 7 tools and 0 that can write anything: no crawl started, no setting changed, nothing marked done.

Drafting needs no data connection, and it carries risks of its own; our guide to writing with AI without hurting rankings covers them.

Which AI SEO tool should you start with?

Start from the job, not the tool. Each job has its own reference figure to hold the tool against.

If the job is	Start with a tool that	Check its figures against
Finding what is broken on your site	Shows which check produced each finding	The affected pages, and their Search Console clicks
Sizing demand for a topic	Documents how its volume model works	Search Console impressions for related queries you already appear for
Drafting or scoring a page	Shows what its score compares the draft with	The pages ranking today, read by an editor
Seeing whether AI answers cite you	Tracks AI answers, a separate category	The answers themselves, checked by hand
Asking an assistant about your site	Connects to your data read-only	The same figure on the tool's own screen

GrowthHasten is the wrong place to start if the job needs search volume, keyword difficulty or rank tracking, since it produces none of them. The same goes for white-label or scheduled email reports, which its page says it does not offer, and for crawls of more than 5,000 pages or more than one a day.

The question "where did this number come from?" also works on the next report and the next vendor demo, long after this choice is made. Open the report your team acts on most before Friday and write one word beside each of its three most-used figures: measured, computed, estimated, benchmarked or generated. Whichever figure you cannot label is the one to trace first.