What To Ask Before Hiring An AI SEO Company

Treat markup as something with a maintenance cost rather than a one off implementation. Prices change, people leave, products are discontinued, and structured data quietly keeps asserting the old version long after the visible page has been updated. Adding a schema review to whatever process already updates your pages costs minutes and prevents the most damaging failure mode, which is confidently stating something that is no longer true.

Ask How They Price It Retainers dominate this field and mostly make sense, because the work is continuous and the third party portion is slow. What matters is what the retainer covers and whether the scope is written down in units you can count.

This variability is the main practical trap. Testing without web access and concluding you are invisible measures the training corpus rather than current retrieval, and the two can disagree sharply. Record which mode you used with every run.

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.

Preference is the wrong word, strictly. These systems do not have taste. They reach for sources that match the shape of the answer being written and that contain claims which can be lifted without distortion, and certain formats do that reliably.

A quick way to find contradictions is to write out your key facts on one sheet, taken from your structured data, then check that sheet against your about page, your main directory listing and your marketplace account. Doing it manually feels crude and it surfaces the conflicts that validators never flag, because a validator checks syntax rather than whether your founding year matches the one you published elsewhere.

One inversion is worth noticing in your own analytics. The pages that earn citations are frequently not the pages that earn traffic, and teams optimising purely for sessions will deprioritise exactly the specification and comparison content that this channel uses. Keeping a separate note of which pages appear in citation lists prevents a well performing asset being retired because its visit numbers looked unremarkable.

The fix is straightforward if slightly humbling. Pull the language from sales call notes, support tickets and the search queries in Search Console, then have somebody outside marketing read the prompt set and flag anything that sounds like a brochure.

The defensible position is to spend an hour on it if you like, and to spend the rest of the week on the things every system already reads: accessible pages, accurate Organization markup, consistent identity and content a machine can quote.

The answer engine optimization you want describes a baseline: a prompt set built from how your customers actually speak, run across the assistants that matter, with raw answers and cited sources recorded. Everything after that should be justified by reference to what the baseline showed.

What Structured Data Is Doing Here Markup removes ambiguity. Prose says your company was founded in 2011 and operates in three counties, and a machine has to parse that from language. Structured data states it as a field, with no inference required.

Buy the technical audit if nobody on the team reads server logs, and buy the third party source work unless you already have a functioning public relations capability. Those are the two areas where the learning curve is steep and the cost of getting it wrong is highest.

Writing Prompts That Sound Like Customers The foundational skill is deceptively mundane. Somebody has to write the questions your buyers actually ask, in their words, without the category vocabulary your team uses internally.

Format choice also has a maintenance implication that gets overlooked. Specification and comparison content decays fastest because it contains the numbers that change, so choosing these formats commits you to reviewing them. A comparison page nobody has updated in two years can be cited with its outdated figures attached to your name, which is worse than never having published it.

The missing skill is the reflex to ask for the sample size and the publisher before repeating a figure, and to attribute it when using it. Teams that skip this end up presenting a vendor's marketing to their own board as market data, which is a difficult position to recover from.

One final practical check costs nothing. Ask for a client reference in a category structurally similar to yours rather than a famous name, and when you speak to them ask what the agency got wrong rather than what went well. References are chosen to be positive, so the useful information is in how candidly they describe the difficult parts.

What to Do About llms.txt and Similar Files Proposals for machine readable files aimed specifically at language model consumers appear periodically. Adoption is inconsistent and support varies by provider, so treat these as low cost and speculative rather than as a requirement.