Answer in brief
Being cited in an AI answer is not a ranking, and the work that earns it is not the work that earned rankings. The price range for it runs from $150 to $2,500, and the difference between the ends is not effort but scope.
A different question with a different answer
Classic search optimisation answers one question: when someone types this, where does my page appear in a list? AI search answers a different one: when someone asks this, does my page get quoted in the reply? The first is about position. The second is about being usable as a source, which is a property of the text itself rather than of the page's standing.
That difference is why the VIT MARKET catalogue files the work as two separate positions rather than one. Preparing a site for AI crawlers — robots.txt, llms.txt and rendering — is listed at $150 to $450 for 4 to 8 hours and marked beginner. Actually competing for placement in ChatGPT, Perplexity and AI Overviews is listed at $300 to $2,500, takes 3 to 5 days to start plus 3 to 5 hours a week to maintain, and carries the difficulty label "expensive and complex".
The gap between those two entries is the whole subject. One is a plumbing job with a defined end. The other is an ongoing editorial commitment, and buying the first while expecting the results of the second is the most common way this budget gets wasted.
What the cheap end actually fixes
The $150 to $450 entry is infrastructure, and it is worth doing first because everything else depends on it. An AI crawler that cannot read a page cannot quote it, and a surprising share of modern sites are unreadable to them for reasons that have nothing to do with content quality.
The three named deliverables each solve a specific failure. A robots.txt that has never been reviewed against AI crawler user agents may be blocking them by inheritance from a rule written years ago for a different bot. An llms.txt file tells a model-driven reader which pages on the site are the canonical answers, rather than leaving it to infer that from navigation. And rendering is the one that catches most people: a page whose content only exists after JavaScript runs may be an empty document to a fetcher that does not execute it.
Four to eight hours is realistic for this because it is inspection and configuration, not writing. It has a definite end state, it can be verified, and it does not need to be repeated monthly. It is also the reason the catalogue marks it beginner: the work is unambiguous once someone knows to look.
What it does not do is make anything worth quoting. It removes the reasons a good answer would be skipped. That is necessary and it is not sufficient.
Why the expensive end is priced like a subscription
The $300 to $2,500 position is the one that behaves unlike any classic SEO line item, because of the maintenance clause: 3 to 5 days to start, then 3 to 5 hours every week. Ongoing hours in a price list usually signal reporting overhead. Here they signal something else.
A model's answer to a question is assembled fresh each time from whatever it can currently retrieve and whatever it was trained on. There is no cached position to defend. A page that was quoted in March can be absent in May without anything happening to the page, because a better-structured competitor appeared, or the question itself drifted. Visibility in this channel behaves less like a rank and more like an inventory that has to be restocked.
The weekly hours therefore buy monitoring of a specific kind: asking the actual questions, in the actual assistants, and reading what comes back. Not a dashboard number, but the text of the answer and the list of sources under it. That is the only place the result is visible, and it is why this work cannot be automated into a monthly PDF.
The catalogue's own comparison points are instructive here. It notes that basic GEO packages sell on Fiverr for roughly $156 to $291, while full programmes from proven contractors run $500 to $3,000 and above. The low end of that market is almost always the infrastructure job sold under the more exciting name.
What makes a passage quotable
Being cited requires a passage that can be lifted out of a page and still be true and useful on its own. That single requirement rewrites most of the house style rules that classic optimisation encouraged.
A sentence that depends on the previous three paragraphs for its meaning is a bad candidate, because the retrieval that finds it will not bring the previous three paragraphs with it. A claim without a number, a date or a named subject is a bad candidate, because it is indistinguishable from a hundred other pages saying the same thing. A heading that says "Our approach" is a bad candidate, because nobody asks that question.
The inverse is the working rule: write the answer to a question a person would actually ask, put it directly under a heading that states the question, make it complete inside three sentences, and attach something checkable to it — a figure, a date, a source, a constraint. The rest of the article can be as discursive as you like, as long as the quotable units exist.
This is why the catalogue puts schema markup ($120 to $500, 4 to 8 hours across 5 to 7 templates) in the same family. Structured data does not make text quotable, but it tells a machine reader what kind of thing the page is describing, which is the difference between a passage that is retrieved for the right question and one retrieved for the wrong one.
Speed still matters, for a colder reason
The Core Web Vitals entry — $200 to $800, one day of diagnosis plus one to three days of implementation — looks like a classic performance job, and for human visitors it is. In the AI-search context it earns its place for a blunter reason: a fetcher on a budget gives up.
Retrieval systems operate under time limits. A page that takes several seconds to become readable competes against pages that take a few hundred milliseconds, and the competition is not judged on patience. Slow pages are quietly under-represented in the corpus that answers get built from, and no amount of good writing compensates for not being read.
The diagnosis-then-implementation split in that price line reflects how the work actually goes. The first day establishes what is slow and why, which is frequently not what the team assumed. The next one to three days fix it. Selling the fix without the diagnosis is how teams end up optimising the wrong asset.
Semantics, and the question nobody typed
The semantic core entry — $150 to $700 for one to two days across 1,000 to 3,000 queries, clustered by what the results actually show — is the least glamorous item on the list and the one that most often decides the outcome.
Clustering by search results rather than by keyword similarity matters because two phrases that look related can return entirely different pages, which means they are different questions wearing similar words. Building one page for both guarantees it serves neither well. In an AI-answer context the penalty is sharper: a page that half-answers two questions is quotable for neither, because the retrieved passage will always be missing the half that mattered.
The quarterly content plan with briefs written for AI answers ($200 to $700, one to two days) is the operational half of the same idea. A brief that specifies the question, the answer, and the checkable fact that supports it produces a quotable article. A brief that specifies a keyword and a word count produces filler with a keyword in it.
Programmatic pages: the highest ceiling and the sharpest edge
The programmatic SEO entry — $500 to $2,500 for a pilot of 500 to 1,000 pages in three to seven days — is marked "expensive and complex", and the label is doing real work. This is the technique with the best return when it fits and the worst consequences when it does not.
It fits when there is a real matrix of distinct questions: a service in a hundred cities, a product across dozens of compatible models, a comparison table with genuinely different rows. Each generated page answers a question somebody actually asks, and the generation is a distribution mechanism rather than a content strategy.
It stops fitting the moment the pages differ only by a swapped noun. Mass-produced near-duplicates are precisely what search engines classify as scaled content abuse, and the penalty attaches to the domain rather than to the offending pages. The catalogue's insistence on a pilot of 500 to 1,000 pages rather than a full rollout is the correct shape for that risk: publish a slice, measure whether the slice earns anything, and only then continue.
The AI-answer angle adds one more constraint. A generated page is quotable only if it contains a fact specific to its own row — this city's actual delivery window, this model's actual dimension. Templates that generalise across every row produce a thousand pages with nothing quotable in any of them.
The audit that should come first
The technical audit with an implementation plan sits at $150 to $900, one to two days for a site up to 1,000 pages and three to four days beyond 10,000. The catalogue notes that comparable audits go for $300 to $800 on Upwork, which makes it a useful calibration point for the whole list.
The phrase that matters in that entry is "with an implementation plan". An audit that ends in a list of problems transfers work rather than removing it, and the recipient is usually the person least equipped to prioritise it. An audit that ends in an ordered plan — what to fix, in what order, at what estimated cost — is a decision-making document.
For any site older than a couple of years, this is the correct first purchase in the group. It converts an unbounded question, why is this site underperforming, into a bounded list with prices attached. Buying visibility work before knowing whether the site is even readable is how budgets get spent on the wrong layer.
A sane order of operations
Read the site before optimising it: technical audit first, because everything downstream is guesswork without it. Then unblock the machine readers: robots, llms.txt and rendering, four to eight hours, cheap and definite. Then fix what the audit found is slow, since unread pages cannot be cited.
Only then does the content work pay off: semantics clustered by real results, a content plan whose briefs specify questions and checkable facts, schema so machine readers know what they are looking at. And only then, with all of that in place, does the ongoing GEO commitment make sense — because at that point the weekly hours are spent improving answers rather than discovering that nothing was readable in the first place.
The single most useful discipline in this whole category is refusing to buy the maintenance subscription before the one-off jobs are done. The subscription is genuinely valuable and genuinely open-ended, and open-ended spending on an unfixed foundation is the fastest way to conclude, wrongly, that none of this works.
Questions and answers
Is AI search optimisation just SEO with a new name?
No, and the difference is measurable in the deliverables. Classic optimisation competes for a position in a list; AI visibility competes to be quoted inside an answer. That rewards self-contained passages with checkable facts rather than pages with authority signals, and it has no cached position to defend, which is why the catalogue prices it with ongoing weekly hours rather than as a one-off.
Why do GEO prices range from $156 to over $3,000?
Because two different jobs are sold under one name. The cheap end is usually the infrastructure task — robots.txt, llms.txt and rendering, listed separately at $150 to $450 for 4 to 8 hours — which has a definite end. The expensive end is an ongoing programme: 3 to 5 days to start plus 3 to 5 hours a week of asking real questions in real assistants and improving what comes back.
What should be bought first?
The technical audit with an implementation plan, at $150 to $900. It converts an unbounded question into an ordered list with costs attached. Buying visibility work before knowing whether the site is readable at all is how budgets get spent on the wrong layer.
When is programmatic SEO safe to use?
When the generated pages answer genuinely different questions and each contains a fact specific to its own row. When pages differ only by a swapped noun, they are near-duplicates, and search engines treat mass-produced near-duplicates as scaled content abuse at the domain level. That is why the catalogue sells a pilot of 500 to 1,000 pages rather than a full rollout.