What To Ask Before Hiring An AI SEO Company

Comparison Is the Native Format Shopping questions are comparison questions. Somebody asking what to buy wants options weighed against each other, so the sources that get used are the ones that have already weighed them.

Log the conditions with every run, including which assistant, which mode, whether web access was enabled and the date. When a result moves sharply, the conditions log is usually what tells you whether the world changed or your setup did.

Identifiers Have to Be Stable and Consistent A product needs to be recognisable as the same product across your site, marketplaces, retailer listings and review coverage. Where the naming drifts, mentions fail to accumulate and no single product ever reaches the confidence needed to be named.

It does not contain a return on investment figure calculated from an assumed conversion rate applied to an estimated mention volume. That calculation looks rigorous and is a chain of guesses, and it will not survive the first person who asks where the first number came from.

Ask Who Writes and Who Reviews Find out whether the writing is done by somebody with subject knowledge or generated and lightly edited. Both happen, and the second is not automatically disqualifying, but you need to know because you are the one who carries the liability for inaccurate claims about your own products.

Testing too rarely means you find out about a problem a quarter after it started. Testing too often means drowning in variance that looks like signal and reacting to noise. Both failures are common and the second is more expensive, because it produces work.

Where Marketplaces Fit Marketplace listings are frequently cited, and they are a mixed blessing. They provide corroboration and structured data you did not have to build, and they put a description of your product in circulation that you only partly control.

This means a single answer is a sample. Being absent once is not evidence of a problem and being named once is not evidence of success, and treating either as a result is the most common analytical error in this field.

Finally, be prepared for the teardown to produce a finding nobody wants. Sometimes the competitor is genuinely better documented because they have been answering customer questions in public for years while your team answered them on the phone. There is no shortcut around that, and the only useful response is to start doing the same thing now rather than looking for a technical explanation that would be easier to fix.

Build the run into an existing routine rather than creating a new one. Measurement programmes in this field fail through quiet abandonment rather than through a decision, and a modest set attached to an established monthly process survives far longer than an ambitious one that depends on somebody remembering to start it.

Watch the source list as closely as the mention rate, because it usually moves first. New citations from a directory you corrected are a leading indicator, and they typically appear a month or two before any change in whether you are recommended.

A frequently quoted comparison showing assistant referrals converting several times better than search came from a vendor selling the service, across 312 business to business brands. A widely shared claim about explosive referral growth rested on nineteen analytics properties. Both are legitimate observations and neither supports the confident generalisation usually attached to them.

Check Whether You Are Even Present Go to each of those recurring sources and look for yourself. The usual outcome is not that you are described badly. It is that you are absent, or listed with an old address, or categorised under something nobody searches for.

There is a defensible way to measure this. It produces less certainty than a paid media report and considerably more than a visibility score, and it has the advantage of surviving scrutiny. ai search optimization

When to Test More Often Three situations justify a tighter loop. During an active campaign where you need to attribute a specific change, weekly runs on a subset of prompts are reasonable, provided you accept the variance.

Where It Overlaps With Classic SEO A good deal of the groundwork is shared. Crawlable pages, sensible internal linking, fast rendering, accurate structured data and a clean information architecture all help both a search crawler and an AI crawler. If your site fails those basics, an agency will fix them first, and you should be suspicious of anyone who skips straight to the exotic work.

The honest position is that attribution in this channel is harder than in any other you are currently running, and the field has responded to that difficulty mostly by inventing numbers. Confident figures circulate widely, and a surprising share of them trace back to a vendor's own sample or to a study far smaller than the claim implies.

Content quality also carries across. Pages written to answer a real question, with specifics and figures and a clear point of view, perform better in both channels. The overlap is real enough that a competent traditional SEO team can learn this work. The gap is in measurement and in the parts that have no search equivalent.