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Guide
What an SEO and GEO plugin has to do, how classic vendors earn on AI credits, why your own API key costs a fraction, what schema and head meta actually do, how Googlebot works and how language models pull information.
By Nami Shams · about 25 minutes to read
An SEO and GEO plugin does two related jobs inside your own website. It makes sure search engines read your pages correctly and show them in their result lists. And it makes sure generative assistants find your content, accept it as a source and name you when somebody asks about what you offer. The groundwork is the same. What counts as success is not.
Search engine optimization is the work of getting a page to appear in a list of results — as high as possible, with a title and a description that invite a click. Success is readable: a position, a number of impressions, a click-through rate. The chain behind it has been stable for years: a program visits your page, reads the source, follows the links, files the result in a database, and later decides where that entry appears for which query.
Generative Engine Optimization has a different goal. When somebody asks an assistant which tool or supplier suits a particular problem, they do not get a list of ten entries. They get a paragraph with two or three names. GEO is the work of being one of them. Success cannot be read off a position, because there is no position — only off whether your brand appears in the answer and, if not, who stands there instead.
It is tempting to treat GEO as something entirely new. It is not. A model writing an answer draws on text it either saw during training or fetched through a search at the moment of the question. In both cases the same kind of page comes through well: clearly structured, with an unambiguous answer close to the question, facts stated in machine-readable form, no technical obstacles. Anyone who builds pages properly for classic search has already done most of the GEO work. What is missing is the other measure. Running two separate tools makes little sense: the foundations are shared.
External SEO tools work from the outside. They crawl your website like a visitor and hand back an evaluation — a report in a browser or a CSV file. Then the actual work begins: you carry the findings back into your WordPress by hand. With thirty pages that is fine. With three thousand it is not.
A plugin sits inside the data source instead. It does not read the delivered HTML; it reads the post, the meta fields, the taxonomies, the product data — and it can write back. It knows the difference between a post, a page, a product and a category archive, because WordPress knows it. It sees an accidental noindex the moment it is set, not at the next crawl four weeks later. And it can offer the correction where the mistake was made: in the editor. That closeness to the data is the decisive advantage.
The history of this discipline comes in four stages. In the late nineties, visibility lived in a field called meta keywords: you typed in what you wanted to be found for. That worked until too many abused it. Then came PageRank — a page's importance derived from the links pointing at it. What mattered now was who writes about you, not what you claim about yourself.
The third stage began when search engines started showing answers inside the result list: featured snippets, knowledge panels, answer boxes. Many learned there that a short, directly worded answer can be worth more than a long run-up. The fourth stage is the one we are in: the answer no longer sits above the list, it replaces it — and it names sources, but not ten. The task is not fundamentally different, only narrower.
llms-full.txt, ships the content with it. More on the feature page.The list below is a checklist. Take it through every product you are considering — ours included. Each point says why it counts and how to tell whether it has been solved properly.
The title is the headline of your entry in a result list; the description is the text underneath. Both have to be settable by template for a whole content type — and overridable per page. The mark of quality: the template previews the values filled in, not just the placeholders, and the override sits in the editor rather than a separate menu.
With noindex, nofollow, noarchive, nosnippet and noimageindex you tell a search engine what it may do with a page. Canonicals decide which of several near-identical addresses is authoritative. Pagination is about page two of a listing not competing with page one. Done well means: visibly set rather than guessed at, the effect explained in a sentence.
The sitemap is the list of addresses you offer a search engine. It has to update on publish, without anybody pressing a button. And it has to respect your robots decisions: a page set to noindex does not belong in the sitemap, or you send two contradictory signals. Check this point specifically — it is often overlooked.
Structured data is machine-readable information in the page head, usually JSON-LD: what this is, who wrote it, what it costs, when it takes place. Search engines build richer displays from it; language models pull facts out of it. The preview is what matters — you have to see which JSON-LD is actually delivered, rather than trusting a form. AI Rank Suite covers 77 schema.org types and shows the generated JSON-LD live.
A list of every page with a rating is the only way to keep an overview across hundreds of addresses. More important than the score is the filter: show me everything accidentally set to noindex, everything without a description, everything with broken links. An overall grade alone does not help — you need the pages you can fix today. This is how the inventory is built.
Advice works when it appears where the writing happens. A suggestion for the description while the text is being written gets acted on; the same one in a report opened next week usually does not. Look for a rating that updates without a save and reload — and that can be switched off when it gets in the way.
These two files are your offer to language models: a compact map of your website and, in the long variant, the content with it. They do not replace good pages, but they spare a model the guesswork. Google's PageSpeed audit checks several related points under the name "Agentic Browsing". A good plugin does not only generate the files — it checks them against that audit and names what is missing.
The AI vendors' crawlers do different things: some collect training data, some fetch a single page at the moment a question is asked, some build a search index of their own. Blocking one has consequences — possibly that your page is missing from exactly the answer you wanted to appear in. A good plugin lists the bots individually and explains, per bot, what a no means. AI Rank Suite carries 31 named AI crawlers.
Everything up to here is preparation. The real GEO question is: does your brand get named when somebody asks — and if not, who instead? That can only be answered by asking: a fixed set of prompts, repeated over time, with a record of which competitors came up. AI Rank Suite runs 25 prompts per visibility run through your own API key. This is what the readout looks like.
Switching must not be a one-way street. An import has to carry over titles, descriptions, robots settings, canonicals and redirects — and be reversible. Coexistence matters just as much: if two plugins both write a title into the page head, you get duplicate output. There needs to be a clear rule about who owns what. AI Rank Suite imports from 14 sources — among them Yoast, Rank Math, All in One SEO, SEOPress, The SEO Framework, Slim SEO, Squirrly, SmartCrawl, WP Meta SEO, Jetpack SEO, WooCommerce and Redirection — with one-click undo and exactly one owner per head output.
If your website runs in several languages, every translation needs its own meta values and its own canonical. A plugin that understands WPML, Polylang or TranslatePress saves you maintaining everything twice. And it has to cope with size: a list across tens of thousands of addresses must not stall the admin. AI Rank Suite has been tested on a site with around a million pages in 15 languages.
The moment AI is involved, text leaves your server. The question is where it goes. Does the request run through the plugin vendor's servers, or straight to a model provider you hold your own contract with? Is the text stored there? Is that written down where you can check it? With AI Rank Suite every request runs through your own key, directly to the provider you chose. Where to begin is in the setup guide; what separates the editions is on the pricing page.
The market for WordPress SEO plugins is old, crowded and largely mature. Choosing a plugin today is rarely a choice between good and bad, but between different ideas of what a plugin should do — and how it should pay for itself. What follows describes the best-known vendors plainly: what each stands for, and, because this decides your bill, how each charges for AI.
One note first. Every figure below comes from the vendors' public pricing pages as of August 2026, for one website and one year. Prices and quotas change often in this market, and introductory discounts distort comparisons further. Check the current terms with the vendor before you decide. This page is a snapshot, not an offer.
Rank Math stands for feature density. It covers schema, redirects, local details and analysis in one package, and has found its audience among people who like turning the knobs themselves. The licence runs at roughly €96 for the first year and €108 on renewal. The AI features sit outside that: a separate subscription between $3.99 and $12.99 a month, with 750 credits included each month at no extra cost. Write more and you move up a tier or buy more.
Yoast is the best-known name in the field and shaped much of what now feels obvious — the readability analysis, the traffic light in the editor, the search result preview. The Premium licence costs $118.80 a year. The AI features are a separate product called "AI+" at $358.80 a year, including five of your own questions and one analysis per week. That number matters for chapter 5, because it makes the price of a single AI request easy to pin down.
All in One SEO is the oldest of the big names and now presents itself as a broad package reaching past optimisation into site management. The entry licence is $99, the Elite tier $599. AI is billed through a credit system, with 10,000 to 200,000 credits depending on tier. How a credit relates to a token is not something an outsider can readily calculate — more on that next.
SEOPress is the lean, modestly priced entry in the field, and it takes care not to write much clutter into your database. The licence costs $49, the Unlimited variant $149. Notable here: SEOPress ships an llms.txt — the file a website uses to tell language models what content it offers. That puts it among the classic vendors that picked up on generative search early.
EZY.ai is not a classic SEO plugin but a hosted service that watches your visibility in AI answers. It costs $348 a year, or $29 a month, and tracks ten questions. The approach differs from a plugin: the analysis runs outside your WordPress installation, and there is nothing for you to maintain.
AI Rank Suite costs $99 a year for one website, or $199 for the agency licence covering unlimited sites. There is no credit system. The AI features run on your own API key, which you create with the provider of your choice and connect in a few minutes. The licence pays for the software; model usage you settle directly with the provider, at list price. Chapter 5 works out what that amounts to, step by step.
| Vendor | Price per year | How AI is billed |
|---|---|---|
| Rank Math | approx. €96 (year 1), €108 renewal | Separate AI subscription, $3.99–12.99/month, 750 free credits monthly |
| Yoast SEO Premium | $118.80 | AI only through the "AI+" add-on |
| Yoast "AI+" | $358.80 | Includes 5 of your own questions and one analysis per week |
| All in One SEO | $99 (Elite $599) | Credit system, 10,000–200,000 credits by tier |
| SEOPress | $49 (Unlimited $149) | No AI allowance stated; includes llms.txt |
| EZY.ai | $348 ($29/month) | Hosted service, 10 tracked questions included |
| AI Rank Suite | $99 (agency $199, unlimited) | No credit system, your own API key |
Every one of these vendors has sound reasons for the choice it made. An allowance is easier to explain and, for many customers, more convenient; your own key is cheaper but asks for a setup step. Which path suits you depends less on price than on how much you actually intend to use. The tiers for AI Rank Suite are on the pricing page.
Anyone buying a credit allowance for the first time usually asks two things: what exactly am I buying, and why isn't it just priced in dollars? Both have unremarkable answers. There is no conspiracy here, just a business model with understandable constraints.
Token. Language models don't read and write in letters but in tokens — small pieces of text, usually half a word to a whole one. An average English sentence is roughly 15 to 25 tokens. Billing is always per token, never per request.
Context. The context is everything the model has in front of it for a single request: your question, the page content sent along with it, the plugin's instructions. The more you send, the more the request costs — even when the answer comes back short.
Input and output price. Model providers charge for reading in and writing out separately, and output is almost always considerably more expensive than input, often by a factor of three to five. A request with a lot of context and a short answer therefore costs less than one with little context and a long answer.
Rate limit. Every account has a ceiling on how many requests or tokens get through per minute. It is not a billing figure but a technical brake — it decides how quickly a run across many pages finishes.
A plugin vendor that builds in AI features buys tokens wholesale and passes them on in portions. Real costs sit in between: servers, queues, caches, monitoring, abuse prevention and above all support — every error message from a model provider lands in their inbox, not yours. Add a legitimate need for predictability: a flat price with unlimited use would be an uncontrollable risk, because a single very active customer can eat the margin of an entire tier. An allowance caps that exposure and keeps the maths stable. That is reasonable, and common across many industries.
The other side is carried by the customer, in five ways. First, the ceiling arrives mid-task: allowances rarely run out at a convenient moment. Second, topping up almost always costs more per unit than what the plan includes. Third, the choice of model belongs to the vendor, who can change it without you noticing. Fourth, the route your data takes — your content passes through the plugin vendor's servers, which depending on your industry is worth checking. Fifth, transparency: a credit is a private currency, and until it is published how many tokens a credit represents, the unit price cannot be compared with a model provider's list price.
Now for the arithmetic. Everything that follows is explicitly a worked example with stated assumptions. Your real costs depend on the model, the length of your pages and how much you use it. The point is not to promise a number but to show the method, so you can run it with your own values.
Assume one visibility measurement with 25 questions, roughly 500 input tokens and 700 output tokens per question, and an inexpensive model at $0.15 per million input tokens and $0.60 per million output tokens.
The method, in four steps:
Total: $0.001875 + $0.0105 = $0.012375 for one complete run across all 25 questions. From here we round up to $0.013, to err high rather than low. That is about one and a half cents for a complete measurement.
Run the measurement weekly and you reach 52 × $0.013 = $0.68 a year, under seventy cents. Run it monthly and you reach 12 × $0.013 = $0.16 a year. Both figures vary with the model and the length of your text.
A measurement alone is not the whole of your usage. Assume that over a year you also have the AI writer produce 300 meta descriptions and 50 article drafts, at the same token prices.
For a meta description, assume 400 input tokens (page excerpt plus instruction) and 60 output tokens:
For an article draft, assume 800 input tokens (brief plus outline) and 2,000 output tokens, roughly 1,400 words:
Writing budget together: $0.0288 + $0.066 ≈ $0.10 a year. Added to the weekly measurement, that lands at roughly $0.78 — with an inexpensive model at this level of use. With a stronger model, longer pages or more writing, the realistic corridor is more like $1 to $5 a year.
Yoast "AI+" costs $358.80 a year and includes five of your own questions per week. Five questions times 52 weeks is 260 questions a year. $358.80 ÷ 260 = $1.38 per question. That figure is not an estimate; it follows directly from the published terms. Nor is it a verdict on the product: the price also covers the interface, the analysis, support and operations. It only shows how differently two billing models price the same operation.
| Vendor | Cost per question | Basis |
|---|---|---|
| Yoast "AI+" | $1.38 | $358.80 ÷ 260 questions a year (5 per week) |
| EZY.ai | $34.80 per tracked question per year | $348 ÷ 10 tracked questions; run frequency not stated comparably |
| AI Rank Suite | approx. $0.0005 in tokens, plus licence | $0.012375 ÷ 25 questions; worked example, number of questions not capped |
| Rank Math | No comparable feature stated | AI subscription targets text generation, not tracked visibility questions |
| All in One SEO | No comparable feature stated | Credits with no publicly calculable token ratio |
| SEOPress | No comparable feature stated | No AI allowance stated in the plans |
It would be dishonest to show the allowance model only from its expensive side. For a good share of users it is simply the better choice. If you never want to create an API key, would rather not receive a second invoice from a second company, or work somewhere every additional vendor relationship has to pass a review — an allowance is more convenient, and convenience legitimately costs something. It also gives you a hard ceiling, a genuine advantage for budgeting.
For AI Rank Suite Pro, this worked example puts the first year at $99 for the licence plus roughly $1 to $5 in tokens, depending on model, text length and usage — call it $100 to $104 all in. For comparison, at the list prices above: Yoast Premium with "AI+" comes to $118.80 + $358.80 = $477.60. Rank Math is roughly €96 for the licence plus $48 to $156 a year for the AI subscription. All in One SEO sits between $99 and $599 depending on tier. EZY.ai is $348 for ten tracked questions. SEOPress is the cheapest entry at $49, but offers no AI allowance.
None of this says which plugin will produce better results for your website — that depends on your content, not the tool. It only says where the money goes. The full tiers are on the pricing page.
Visitors see the <body>; nobody sees the <head>. It holds no content, only statements about the content. If the body is the product, the head is the label — and machines read it first.
The <title> sets the browser tab, the blue clickable line in a search result, and the name an assistant cites you by. Each page needs exactly one, and all should differ. Budget 30 to 60 characters — an approximation, because Google measures pixels. What works is topic, benefit, brand.
Google rewrites titles regularly: if yours describes the page badly, it is replaced by the <h1> or the text other sites link to you with. Triggers are keyword strings without grammar, the same boilerplate everywhere, and titles unrelated to the content. Write descriptive, distinct titles and rewrites become rare.
The <meta name="description"> is not a ranking factor; Google has said so repeatedly. It still decides your click-through rate — the only prose a searcher reads before clicking. Roughly 80 to 160 characters, a complete sentence with a benefit and a prompt. It is replaced even more often than the title, because Google looks for a snippet matching the query; omit it and Google grabs any paragraph.
<meta name="robots"> says what may happen to a fetched page: index/noindex, follow/nofollow, plus max-snippet, max-image-preview and noarchive; with nothing specified, the default is index and follow. The same directives sent as an HTTP header are the X-Robots-Tag — the only option for files with no HTML head: PDFs, images, downloads.
rel="canonical" answers which of several addresses with the same content is the real one. The normal case is self-referential — every page points at itself, the best insurance against parameter duplicates. A canonical pointing blanket-style at the homepage turns every subpage into the homepage, and your index melts to one address; a canonical plus noindex is a contradiction that can carry the noindex to the canonical target. Either way a canonical is a strong hint, not a command: Google picks for itself.
With several language versions, hreflang assigns which is meant for whom. Three rules: references must be reciprocal, each page must list itself, addresses must be absolute. The code is a language (en) or language and region (en-GB), with x-default catching the rest.
Open Graph is why a link in WhatsApp or Slack appears as a card with an image, not a bare URL: og:title, og:description, og:image (1200 × 630 pixels is safe), og:url, og:type. X adds its own; twitter:card set to summary_large_image decides the large rendering there. <link rel="alternate"> covers the same in other forms: RSS feed, hreflang versions. <meta charset="utf-8"> belongs in the first 1024 bytes, or the browser guesses and your accented characters break. And the viewport with width=device-width is the precondition for a usable phone rendering — since Google evaluates mobile first, its absence is a ranking problem.
The most common defect in a site grown over years is not a missing tag but a duplicated one: SEO plugin, theme and page builder all write into the same head, leaving two titles, two canonicals and three og:image values. Which wins is not specified — most readers take the first, some services the last; and if the canonicals differ, it gets serious. The fix is organisational: one owner per output. That is how the SEO suite in AI Rank Suite is built: it detects existing output and stands down.
Someone reading £39 knows instantly it is a price; a machine sees two digits and a symbol. Structured data closes that gap: it states what a thing is and how it relates to other things. schema.org is the shared vocabulary for this, run by the major search engines since 2011.
Of the three notations, Microdata and RDFa are woven into the HTML; JSON-LD sits alongside as its own block. Google recommends JSON-LD for a practical reason: the data does not hang off the presentation. Swap the theme and the block survives; Microdata tears away with a redesign.
What you want is a graph: a list in which each entity is described once and everything else refers to it by @id, rather than the organisation written out again in every block. Organization describes who you are; WebSite the site, with the organisation as publisher; WebPage the individual page. On top sits the content type: Article with author and dates, Product with offers and availability, LocalBusiness with address and hours, FAQPage with question-and-answer pairs, BreadcrumbList with the path from the homepage. Instead of repeating the organisation inside the article, you write only "publisher": {"@id": "…/#organization"}.
Rich results are the enriched presentations: stars, expandable questions, breadcrumbs, price and availability. Structured data is the precondition, not a guarantee: Google decides per query and device, and has repeatedly narrowed which pages qualify. You influence eligibility, not whether it is granted.
Four mistakes recur. Invented ratings: an aggregateRating built from stars that exist nowhere is a guidelines violation and a realistic trigger for a manual action. Schema for content that is not on the page: an FAQPage with questions the visitor never sees — structured data describes what is visible, it adds nothing. Duplicate nodes: if theme and plugin both emit an Organization, two companies stand on the page. Missing required fields: a Product without offers gets read but not featured.
For classic search, schema is a presentation aid; for language models it is a statement in a form that needs no interpretation. A model asked to infer from prose whether "from £39" is the monthly, entry or promotional price is guessing; an Offer node with price and availability leaves nothing to guess. Schema makes entities and relationships machine-readable — exactly what the retrieval layer behind an assistant thinks in. Whether a given model reads your JSON-LD cannot be proven from outside; that unambiguous data allows fewer misreadings can. AI Rank Suite ships 77 schema.org types, each with a live JSON-LD preview.
Between publishing and appearing in the results sit three steps that fail independently: crawling, rendering, indexing — then ranking.
Crawling. Googlebot discovers addresses through links, XML sitemaps and earlier visits; a page with no inbound links and no sitemap entry does not exist for it. robots.txt controls what it may fetch — a request, not a lock. Crawl budget follows from what your server tolerates and the demand Google assumes: at a few thousand addresses a non-issue, as Google says itself. It matters when filter parameters generate millions of near-identical addresses.
Rendering. Google works in two waves. First the delivered HTML is read; anything JavaScript loads later in the browser is missing from it. Those pages go into a queue and are rendered later with headless Chromium. Usually that works — but not when a script throws an error, content appears only after a click, or navigation runs through click handlers instead of real <a href> links.
Indexing. Here Google decides whether a page is taken in and which of several similar addresses is canonical — and here sits the most consequential misconception in technical SEO. noindex is an instruction inside the page and has to be read to work; Disallow prevents that reading. Block and set noindex together and you cancel the noindex. Worse: a blocked address can still land in the index if others link to it. The right order is allow the fetch, set noindex, wait until the page is gone, then block.
Ranking and control. What decides position fits no formula; the categories are relevance, quality and trustworthiness, links from other sites, freshness, user signals and Core Web Vitals — the last explicitly a small factor that can tip otherwise equivalent results. Google's own view is visible only in Search Console: URL inspection tells you whether an address was crawled and indexed and which Google chose as canonical. AI Rank Suite connects it and Analytics read-only.
Googlebot fills the search index; block it and you disappear from Google Search. Google-Extended is not a crawler but a robots.txt token: it governs whether already-fetched content may be used for training and grounding Gemini. Blocking it does not remove you from search — nor from the AI overviews, which draw on the search index. Confuse the names and you block the wrong thing. The crawler control lists 31 named AI crawlers with an explanation per bot and a daily log of actual visits.
Language models have two different sources of knowledge, and most misunderstandings about "optimising for AI" come from mixing them up. Training knowledge sits in the weights: fuzzy, with a cut-off date, with no citation — an average of what the web said about you, not your homepage today. Retrieval at runtime is the opposite: the assistant searches while it answers and writes from the pages it fetches. Everything current comes from there — the only path you can influence in reasonable time.
Simplified: the model first decides whether it needs to search at all. If so, it breaks your question into several search queries, often worded differently from yours — "which SEO plugin is worth it for a small site" becomes separate queries about comparisons, pricing and features. A search service returns candidates; pages are fetched, split into passages and ranked by fit. Only the best passages reach the context, and the sources behind load-bearing sentences get cited.
What gets picked is not the page with the most occurrences of a term but the passage that visibly answers a sub-question: the retrieval layer works semantically, and the summariser needs a sentence you can lift without repairing it. So phrase headings as the question somebody asks, and answer it in the first sentence underneath, not the fourth paragraph. Keep facts together — price, condition and limitation in one passage. And use structure: short paragraphs, lists, tables.
Models also reason in entities rather than strings. An entity becomes stable when several independent sources say the same thing about it. If your company name reads one way on the website and another in a directory, it fragments into several uncertain ones, and a model then says nothing concrete about you. Consistent details across sources outrank most writing tricks.
llms.txt is a proposal, not a ratified standard: a Markdown file at the root that briefly describes your important pages, so a model can grasp a site's structure without wading through navigation and ad slots. llms-full.txt carries the content itself. Whether any vendor reads them is not promised — the honest position is to leave that open. What is documented is Google's PageSpeed audit Agentic Browsing: it checks whether such a file is reachable, contains valid Markdown and names resolvable links. AI Rank Suite generates both files and tests them against those three criteria — details under llms.txt, the step-by-step setup in the guide.
Asked twice, the same question rarely produces the same answer: generation is not deterministic, the results behind it shift, models get swapped. Ask once whether an assistant mentions you and you have an anecdote, not a measurement — what works is a fixed set of questions repeated over time. AI Rank Suite runs 25 prompts per visibility run on your own API key, captures competitors and cited sources and keeps the history — see AI visibility.
Finally, "block the AI bots" is not one decision but three. Training crawlers such as GPTBot, ClaudeBot or the Google-Extended token collect material for future model generations; blocking keeps you out of future training but changes nothing about whether you appear in answers today. Search crawlers such as OAI-SearchBot or PerplexityBot build the index the assistant searches at runtime; block them and you stop appearing in live, cited answers — usually the costliest of the three, and the one most often set by accident. User-triggered fetches such as ChatGPT-User happen only because a person just pasted your link; blocking means that prospect reads "I can't access this page". Good reasons exist to allow one and block another — none to block wholesale.
For two decades a search result was a list of suggestions a human chose from; increasingly it is an answer with two or three names in it. That moves the target: no longer "rank first" but "be one of the named".
If a question is already answered in the answer, nobody clicks. The direction is clear; the magnitude is not — it depends on sector, question type and market, and anyone quoting a universal percentage has estimated it. Measure against your own numbers: in Search Console, steady impressions alongside falling clicks reveal the effect better than any study. Whoever clicks after a summary has a reason to: fewer but better-qualified visits is a different calculation from fewer visits.
Because models aggregate what is written about you, what happens outside your site matters more: comparison articles, forum threads and directory entries feed the same pool, and a consistently covered brand is a solid entity to a model. In parallel come agentic browsers that fetch pages, fill in forms and compare offers. Such a visitor ignores your layout and reads text and structure: a price inside a graphic is not there for it, nor are opening hours in an image. Machine-readable facts — in the visible text and the schema, identical in both — stop being optional.
For small sites this is an opportunity: in the link contest the bigger domain nearly always wins, while an answer engine looks for the passage that answers the sub-question. A precise, well-structured page from a specialist can stand next to a large brand. That is not certain — it is a tendency you should measure on your own site rather than believe.
title, canonical and og: values, and assign one owner per output.Organization, WebSite and WebPage everywhere, plus Article, Product, LocalBusiness, FAQPage and BreadcrumbList where they apply, linked by @id.None of this requires a new website, and none of it promises rankings. What you have is a site that reads unambiguously to crawlers and models alike. A per-page GEO and SEO score shows where you stand; the pricing page covers the tiers.
The free edition installs in a minute and needs no key. Pro is $99 a year — $69.30 for the first year with code LAUNCH30, and if it does not fit, you get your money back.
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