AI SEO & GEO: The Questions Brands Actually Ask

Straight answers to the questions we get asked most about getting recommended by AI assistants — grouped, deduplicated, and written from measuring four engines daily rather than from recycled advice. Where the honest answer is "this doesn't matter as much as people say," we say that. Vonca is our product and it appears where it's genuinely the answer, not in every paragraph.

The basics

What is Generative Engine Optimization (GEO)?

GEO is the practice of getting your brand named and cited inside AI-generated answers — ChatGPT, Perplexity, Gemini, Claude, Google's AI Overviews — rather than ranking a blue link. The unit of success is a citation inside an answer, not a position on a results page. "AI SEO," "LLM optimization" and "generative search optimization" all describe the same work; GEO and AEO (answer engine optimization) are the two labels that stuck.

What is the difference between SEO and GEO?

SEO optimises for a ranked list of links; GEO optimises to be quoted inside a synthesised answer. SEO's unit is the page and its position. GEO's unit is the passage — whether an engine can lift a self-contained sentence from your page and use it. They share a foundation: a page that isn't crawlable and indexed can't be cited either. GEO adds two things on top: passage-level clarity, and corroboration from sources you don't own.

GEO vs AEO — is there a real difference?

Not much in practice. AEO grew out of featured snippets and voice answers; GEO grew out of generative engines. Both mean: answer the question directly, in a form a machine can lift. If a vendor draws a hard line between them, it's usually positioning rather than method.

How do AI assistants choose which brands to recommend?

For a recommendation question, an engine retrieves sources and synthesises what they already say. It rarely elevates a vendor that no retrieved source mentions — a vendor's own page is evidence of what it claims, not evidence that it's a good option. This is why third-party lists, directories and community threads carry weight far out of proportion to their design quality.

How do LLMs decide a source is trustworthy?

There is no published trust score, and anyone quoting one is inventing it. Observably, engines favour sources that are indexed, current, specific, and corroborated elsewhere. A dated page with concrete numbers and named sources gets used more readily than a confident page with none — because a claim that can't be checked is a claim the model has to hedge.

What factors influence AI citations?

In rough order of what we see move: being indexed at all; answering the exact question in the opening lines; being present in the third-party sources that answer already cites; freshness on questions where the answer changes; and internal consistency — the same description of your brand everywhere, rather than a different one on each page.

How often do AI models update their knowledge?

Two clocks. Training data updates on a slow cycle measured in months, and you can't influence it. Live retrieval reads the web now, and you can. That gap is why assistants still recommend companies that shut down years ago — and why a current, dated page can correct the record faster than you'd expect.

Getting visible

How do I optimize my website for ChatGPT?

Three things, in order. Make sure the relevant page is indexed — ChatGPT's search results lean on Bing's index, so submitting via IndexNow and verifying in Bing Webmaster Tools is unusually high-leverage. Put a direct 40–70 word answer to the buying question at the top of the page, before marketing copy. Then get listed on the comparison pages and directories that already appear in those answers.

How can my brand appear in AI search results, fastest?

Edit a page that's already indexed rather than publishing a new one. Search engines recrawl each page on its own schedule, and a homepage is typically the most frequently recrawled page a site owns, while a brand-new URL has to be discovered first — often days to weeks. Adding an FAQ block to your homepage skips the discovery step entirely.

How do I earn AI citations naturally?

Publish something that can only come from you: your own measurements, your own pricing math, a correction to something the market has wrong. Engines cite sources that add information rather than restate it. A summary of five other articles gives a model nothing it can't already produce.

What content format works best for LLMs?

Question as heading, answer immediately below, self-contained enough to make sense quoted alone. Short paragraphs, concrete numbers, no build-up. If a sentence only makes sense after reading the two before it, it won't survive extraction.

How can I optimize blog posts for AI assistants?

Lead with the answer instead of an introduction. Use headings that match how people phrase the question. Include at least one specific figure, date or first-hand observation per section. Link the sources for factual claims — a checkable claim is a usable claim. And date the page honestly.

How do I get my SaaS recommended by ChatGPT?

Get into the sources ChatGPT reads for your category: software directories, alternative-to pages, comparison articles, and relevant community threads. For most B2B categories these carry more weight than your own blog, because "best X" answers are assembled from lists — and a vendor absent from the input lists can't appear in the output.

How can startups appear in AI search results?

Accept that training data can't include you yet and target live retrieval instead. Aim at specific questions where the existing answer pool is thin — a niche use case, a comparison nobody has written, a correction of something outdated — rather than the crowded head query in your category.

When it isn't working

Why isn't my brand showing up in ChatGPT?

Usually one of four: the page isn't indexed; no page answers that question directly; the answer is built from third-party lists you're not on; or the model is working from training data that predates you. Check indexing first — with a site: search or Search Console's URL Inspection. An unindexed page can never be cited, however good it is.

Why is my competitor recommended instead of me?

Look at what the answer cites, not at the competitor. Usually the same handful of comparison articles and directories appear repeatedly, and the competitor is on them. That's a listing problem, not a content-quality problem, and it's fixable in an afternoon — most of those directories accept free submissions.

Why does AI ignore my content?

Common causes, in order: it isn't indexed; the answer is buried below several paragraphs of positioning; it says what twenty other pages already say; or it makes claims with no source, which a model has to hedge rather than repeat. Each has a different fix, so diagnose before rewriting.

How do I become a cited source in Perplexity?

Perplexity leans heavily on live retrieval and cites community discussions and vendor pages more readily than some engines, which makes it the most winnable of the four for a new brand. Target specific questions, keep pages current and dated, and be genuinely useful in the communities it already cites.

Monitoring and measurement

How can I track my brand in ChatGPT and Perplexity?

Manually: ask each engine your buyers' real questions in a clean, logged-out session and log answers with dates. Fine for a handful of queries, unmanageable beyond that — and it can't tell you what changed while you weren't looking. Vonca runs your questions across ChatGPT, Perplexity, Gemini and Claude daily from $99/month and scores each engine separately.

Which AI assistants mention my brand — and does it differ?

It differs more than people expect. You can be cited consistently in one engine and invisible in another for the same question, because they retrieve from different indexes and weigh sources differently. Any single average across engines hides exactly the gap you'd want to fix.

Can AI search traffic be measured?

Partly, and honesty matters here. Click-throughs from chatgpt.com, perplexity.ai and similar sources show up as referrals in GA4, and both Search Console and Bing Webmaster Tools now report AI-surface performance directly. What can't be measured is the answer someone read without clicking. That portion must be modelled and labelled as modelled — a tool reporting exact revenue for it is guessing.

What analytics are available for AI traffic?

Google Search Console's AI performance reporting, Bing Webmaster Tools' AI performance view — which also surfaces the grounding queries Copilot generates — GA4 referral data filtered to AI sources, and server logs for crawler hits. Note that JavaScript analytics can't see AI crawlers, since they don't execute JS; that requires log or edge-level data.

What are the best AI visibility tracking tools?

Profound, Peec AI, Otterly.AI and Semrush's AI Toolkit all monitor mentions, roughly $29–$399+/month, with the enterprise end billing annually. Search Console and Bing Webmaster Tools cover their own surfaces for free and are worth turning on regardless. Vonca tracks four engines at $99/month and adds what monitors leave to you: routing each gap to a specific fix and tying the result back to traffic.

How do I measure AI referrals specifically?

In GA4, segment by source for the AI domains — chatgpt.com, perplexity.ai, copilot.microsoft.com, gemini.google.com and their variants. Volumes look small next to search at first; the useful signal early on is direction and which pages earn them, not the absolute number.

Ecommerce and Shopify

How can Shopify stores optimize for AI search?

Answer buying questions directly on the pages you already have, keep product data complete and consistent, and make sure retrieval crawlers aren't blocked. For app and product recommendations specifically, your app-store listing and third-party comparison pages usually matter more than your blog — that's where those answers are assembled from.

How can AI recommend my products?

Products get recommended when their attributes are unambiguous and corroborated: clear naming, complete specs, consistent pricing across every place you publish it, and presence in the marketplaces and comparison sites engines read. Contradictory information — one price on your site, another in a directory — reliably makes engines hedge or skip you.

How do I optimize product pages for LLMs?

Put the answer to "what is this and who is it for" in the first two sentences, above the marketing. State price, compatibility and constraints in plain text rather than only in images or tabs. Add Product structured data for rich results — useful, but not the mechanism that gets you quoted.

What structured data helps AI understand products?

Product, Offer and AggregateRating are the standards worth implementing, and they earn rich results in classic search. For AI answers specifically, treat schema as supporting evidence rather than the lever: the visible text is what gets retrieved.

Technical questions

Does schema markup help AI assistants?

Less than commonly claimed. Google's own guidance states that structured data is not required for its generative AI features and that there's no special schema to add for them. Keep schema for rich results, but if you're choosing where to spend an hour, spend it on the opening paragraph of the page.

Should I create FAQ pages for LLMs?

FAQ sections on pages that already exist, yes — question-and-answer is the format extraction handles best. Standalone FAQ pages spun up for every keyword variation, no: Google explicitly calls out creating separate content for every possible search variation as scaled content abuse. Add the questions where they belong, and only ones real customers ask.

How important is semantic HTML for AI?

Moderately, and it's cheap. Real headings, lists and paragraphs make passage boundaries obvious; a page built entirely from styled divs makes extraction harder. It's a low ceiling but a low cost — no reason to skip it.

Does robots.txt affect AI crawlers?

Yes, and it's the most consequential file most brands never check. Retrieval bots respect it, so a disallow rule can silently remove you from an engine's answers. Check for accidental blocks on OAI-SearchBot, ChatGPT-User, PerplexityBot, Claude-SearchBot, Google-Extended and Bingbot.

Should AI crawlers be blocked?

Separate the two kinds. Retrieval bots fetch pages to answer a live question and cite you — blocking them removes you from those answers, which is the opposite of what most brands want. Training crawlers ingest content for future model training and involve a real trade-off between exposure and control. Blocking the second doesn't require blocking the first.

Which metadata improves AI understanding?

A title and meta description that state what the page answers, one canonical URL per piece of content, and an honest last-modified date. Beyond that, returns drop off fast — and files like llms.txt are not used by Google Search, so treat them as optional rather than as a lever.

Comparisons

ChatGPT vs Google Search — where should I focus?

Both, but they're reached differently. ChatGPT's search leans on Bing's index, so Bing Webmaster Tools and IndexNow are the fast path there; Google requires Search Console and doesn't use IndexNow at all. Google still sends far more traffic in absolute terms; AI referrals convert differently because the user arrives having already read an evaluation.

Perplexity vs ChatGPT for recommendations

Perplexity retrieves aggressively and shows sources prominently, which makes it the most winnable for a brand with no training-data presence. ChatGPT reaches a much larger audience and blends parametric memory with retrieval, so a new brand takes longer to surface there. For a first citation, Perplexity is usually the realistic target.

Which AI assistant sends the most traffic?

ChatGPT sends the most in most reported datasets, simply by user volume, with Perplexity second for research-style queries. But the split varies enormously by category, and your own GA4 referral data beats any published average — check yours before planning around someone else's.

Choosing tools

What's the best GEO software or AI visibility platform?

It depends on which part of the job you're buying. For monitoring only, Otterly.AI and Peec AI start cheaper. For enterprise reporting and CDN-level integrations, Profound. If you already have Semrush, its AI Toolkit is the least friction. Vonca's case is narrow and specific: one store, four engines, $99/month, and the loop closed from gap to fix to measured traffic — which is the part monitoring tools deliberately leave to you.

Are GEO agencies worth it?

Sometimes — but ask two questions before signing. What exactly will they publish, and will they show you engine-level measurement rather than screenshots? A retainer that produces volume without per-engine tracking is the old content-mill model wearing a new label, and it's the pattern most likely to trip spam policies.

What should I avoid?

Anything promising guaranteed AI rankings, anyone claiming access to internal engine metrics — no third-party tool has that — and bulk directory or link-exchange packages. Also avoid publishing a page per keyword variation: it's explicitly named as scaled content abuse, and the damage outlasts the tactic.

See where you actually stand. The free 60-second scan on the Vonca homepage asks AI engines real buying questions for your domain and shows whether they mention you — no signup, no card. The full product tracks four engines daily from $99/month and turns each gap into a specific fix.