AI SEO services
If a buyer asks ChatGPT, Google AI Mode, Gemini or Perplexity for a shortlist and your competitors keep getting cited and mentioned, a ranking report won't tell you the whole story.
DankRank's AI SEO services are built for that gap. We measure how your brand appears in AI search, look at the sources and signals around those answers, and work on the parts we can realistically improve.
That can involve GEO, AEO, clearer entity information, content, citations, technical discovery, source development and measurement. What we do next depends on what we find first.
Free. Includes 2 meaningful competitors. Founder-reviewed. Delivered within 2 business days. No call required.
01
AI SEO is the work of making a brand easier for search and generative AI answer systems to find, understand, and select, and cite when it is relevant to the question.
GEO, AEO, AIO, LLM SEO. There are already plenty of labels for pieces of this work, and the definitions aren't settled enough to build a strategy around the acronym.
We use AI SEO as the practical umbrella. The questions underneath it are more useful: can the system tell who you are, can it find reliable information about you, do you have pages worth using for the questions buyers ask, and does your brand make it into relevant comparisons or recommendations?
Traditional SEO is still part of that picture, along with entities, source quality, original evidence, technical access and third-party authority.
If you want the acronym breakdown, we keep that separate from the sales pitch.
02
We don't make decisions from one prompt screenshot. It can point us toward something worth checking, but we need to see whether the pattern repeats before we act on it.
We start by defining the questions that matter to the business, then check what happens across the AI and search systems relevant to those questions.
That gives us a record of:
AI answers can vary by wording, model, location, timing and context. We account for that instead of treating a single result as permanent.
whether the pattern repeats
03
AI systems need enough consistent information to make sense of the brand.
We look for gaps and contradictions across the pages you control and the sources around you. Company descriptions, service language, authorship, product or location facts, structured information and third-party references can all create clarity or confusion.
We're looking for places where a machine could reasonably confuse the company, its services or its relationship to the topic. Fixing those gaps is more useful than repeating the brand name everywhere.
AI-search work often comes back to the quality of the pages themselves.
Generic summaries don't give a system much reason to choose your page over somebody else's. Clear ownership, direct answers, original observations, useful comparisons, first-party data, documented experience and better source support give the page more to work with.
We look at what should be improved, what is missing and what never deserved to be published in the first place.
When an AI answer cites sources, we want to know which ones keep earning that role and why.
Sometimes the opportunity is on your own site. Sometimes the answer is stronger third-party coverage, better documentation, original research or a source that explains something better than the current options.
We map the source patterns before recommending the tactic.
Available information set
We track recommendations separately from citations because the commercial value can be very different.
For commercial queries, the more important question may be whether the brand gets included when someone asks for options, comparisons, alternatives or a shortlist.
We also track the language around those appearances because being named for the wrong reason isn't a win.
Content can't help much if the systems that need it can't reliably discover or interpret it.
Depending on the site, we may review crawler access, robots directives, indexing, canonicalization, rendering, structured data and the technical paths AI systems use to discover content.
Technical SEO only expands when the evidence calls for it. We still check this layer early because a discovery problem can make the rest of the plan pointless.
04
What an AI-search program includes depends on the problem, but it can cover:
We won't hand you a spreadsheet of 500 prompts and call that strategy.
I care more about which observations are solid enough to change something than the size of the spreadsheet.
05
AI search can generate a huge amount of data quickly, and plenty of it doesn't deserve attention.
Cultivation OS gives the team one record for the prompts, competitors, citations, sources, hypotheses, changes and follow-up checks behind the engagement.
Claude, OpenAI and purpose-built workflows can help collect and compare repeated observations. Semrush, Google Search Console, GA4 and other search data keep the analysis connected to broader demand and site performance.
Those outputs are reviewed before they become a recommendation.
Some AI-visibility signals look connected without proving that one caused the other. If the evidence isn't strong enough, we keep the idea as a hypothesis instead of turning it into a recommendation.
06
AI visibility doesn't have one universal ranking report.
We build a repeatable view around the questions that matter to the business and track things such as:
We need to be able to compare the same questions over time. That means documenting the changes we make, keeping the prompt set consistent enough to be useful and not treating random output like a trend.
The deeper measurement method lives here:
the prompt set we observed
requires different evidence
07
DankRank launched in 2026, which makes our own domain a useful test subject.
AI Citation Experiment #001 tracks whether a new brand and new domain can earn brand citations in ChatGPT, what sources appear around it and how the result changes as the domain becomes more visible across search.
The experiment page records what happens, including a result that doesn't help the sales story.
08
$2,500 one-time USD
Built for a defined AI-search question or visibility problem that needs a plan before ongoing execution.
The engagement can include a working prompt set, competitor and source analysis, priority findings, recommended actions and a repeatable way to measure what changes.
$3,500/month USD
Ongoing work for brands that need continued measurement, implementation, testing and adjustment as AI search changes.
The exact monthly scope follows the priorities we agree on. If the biggest issue turns out to sit in organic architecture, local search or another part of the system, we scope that honestly rather than hiding it inside an AI-search label.
If you're not sure which one fits, start with the free Baseline before paying us anything.
You get a written founder-reviewed baseline, 2 meaningful competitor comparisons and a clearer view of where you're missing or weak across AI and search. Delivered within 2 business days. No discovery call required.
09
AI-search work is worth considering when buyers use research, comparison or recommendation queries before they contact you and being absent from those answers could affect the shortlist.
That often includes B2B companies, ecommerce brands, multi-location businesses, SaaS, professional services and regulated categories where people need more information before making a decision.
A dedicated AI-search program only makes sense when there is an AI-search issue worth working on.
Major crawl problems, duplicate content, weak location architecture or a lack of useful source material may deserve attention first.
The Baseline helps us decide whether a dedicated AI-search program makes sense at all.
10
At DankRank, AI SEO covers the work that can affect whether a brand is found, understood, cited or recommended in AI search, while keeping the organic foundation underneath it healthy.
That can include GEO, AEO, clearer entity information, source analysis, content, technical discovery, citation visibility and measurement.
The terms overlap enough that the labels are less useful than the job each one describes.
GEO usually refers to generative engine optimization, while AEO usually refers to answer engine optimization. AI SEO is the broader term we use for the client work around AI search visibility, citations, recommendations and the search foundations that support them.
No. Citation behaviour changes, we can't fully see why one source gets chosen over another, and answers can vary between prompts and sessions.
We can improve the inputs we control, study the sources being selected and measure whether visibility changes, but the retrieval and recommendation decision belongs to the system.
We define a repeatable set of relevant prompts and track brand appearances, competitor appearances, citations, sources, recommendation inclusion and other observable signals over time.
We also look at AI referral traffic where it can be identified.
Timing varies too much for one honest answer.
Some changes can be discovered quickly. Citations, recommendations and the sources a system chooses may take longer to change and aren't fully under our control. Timing depends on the starting point, the kind of change being made, how often relevant systems refresh their information and whether the real issue is on your site or somewhere else.
We set the baseline first so we have something real to compare against.
Traditional SEO is still part of the foundation.
AI systems still depend on information they can discover and interpret. Strong organic architecture, useful pages, clear entities, crawlability and authority remain part of the picture.
A weak search foundation usually needs to be fixed as part of the same plan.
Next step
If competitors keep appearing in AI answers and you don't know why, don't start by buying a GEO package.
Send us your site, your primary competitor and a little context.
We'll review how you currently show up across search, compare 2 meaningful competitors and give you a written Visibility Baseline showing where the most useful gaps appear to be.
Free. Founder-reviewed. Delivered within 2 business days. No call required.