Where AI Actually Looks: A Channel-by-Channel Guide to AI Visibility

When a brand first takes AEO seriously, the instinct is to optimize the website — rewrite the pages, add FAQs, tighten the structure. That work matters. But it quietly assumes the AI is reading your site to form its opinion of you. Mostly, it isn’t.

AI engines synthesize an answer from across the web. When someone asks “best project management tool” or “is [brand] any good,” the model draws on Reddit threads, Wikipedia, YouTube reviews, G2 and Trustpilot, news coverage, and a dozen other sources — your own site being just one voice among many. Understanding where AI looks is what separates teams who optimize one page from teams who shape the whole conversation.

Before the channel tour, though, there is a measurement question that determines how much of this you can actually observe rather than infer. It is worth getting straight first, because it changes what the rest of the article is worth to you.

Two very different meanings of “where AI looked”

There are two mechanisms by which a source ends up shaping an answer, and they leave completely different evidence trails.

Retrieval. The engine performs a live search, fetches documents, and composes an answer from them. This is the mechanism described in the original retrieval-augmented generation work (Lewis et al., 2020), and it is what powers Perplexity, Google AI Overviews and the web-search modes of the major chat assistants. Crucially, retrieval leaves a link. The engine can tell you exactly which URL it used, because it fetched it.

Training. The source was part of the corpus the model learned from, months or years ago. It shapes the answer through the model’s weights. There is no link, no fetch, and no record. The influence is real and almost completely unobservable from the outside.

Most “AI cites Reddit heavily” claims conflate these two. Both mechanisms are real, and only one of them is measurable.

Why this matters for your own measurement

Here is where it gets concrete, and where a lot of AI visibility reporting quietly goes wrong.

When a scan runs grounded — the engine actually searches the web — you get real cited URLs. You can see the exact page, check whether it is yours, and compare it against what you rank for.

When a scan runs ungrounded, there is no retrieval step and no link. The best available signal is having an analyzer read the answer text and extract domain names mentioned in prose. That is genuinely useful — it tells you what the model associates with the topic — but it is not a record of what the model fetched, because nothing was fetched.

In LLM Metrix the stored citation row carries no URL at all in the ungrounded case, and any derivation that needs URLs reports that URL data is unavailable rather than running on empty. That guard exists because of a specific and very plausible failure: with zero citation URLs to match against, a naive “which pages do I rank for that no engine cited?” query returns every page you rank for. It renders as a long, alarming, entirely fictitious table. The honest output is “no URL-level data in this scan,” so that is what it says.

So when you read any channel analysis — including this one — ask which mechanism the evidence came from. Citation intelligence shows the sources engines actually named for your prompts, and reading it well means knowing whether those were fetched links or extracted mentions.

Now, the channels.

Reddit: the authentic-opinion engine

AI models lean on Reddit because it is where people give candid, unpolished opinions — exactly what a user wants when they ask “what do people actually think of X.” A single well-regarded thread can shape how an engine characterizes your brand, and Reddit’s structure (a question, ranked answers, visible agreement) is unusually close to the shape of the answer an engine is trying to produce.

You cannot and should not astroturf this. Beyond the obvious ethics, it does not survive contact with the platform’s own spam and manipulation rules, and a caught campaign creates exactly the kind of durable negative coverage you were trying to avoid. What works is genuine participation: being present where your category is discussed, answering questions helpfully, and earning authentic mentions. See Reddit and AI visibility.

Wikipedia and Wikidata: the entity backbone

Wikipedia and Wikidata are foundational to how search and AI systems understand entities — who you are, what category you are in, what is true about you. A factual, well-sourced presence reinforces your brand as a recognized entity, which helps engines describe you accurately and, critically, avoid confusing you with a similarly named company.

The two have very different bars, and that difference is a practical lever most brands never use. Wikipedia’s notability guideline requires significant coverage in multiple independent reliable sources — a high bar most companies genuinely do not clear. Wikidata’s notability policy is materially more permissive: an item qualifies if it is a clearly identifiable entity that can be described using at least one serious, publicly available reference. Plenty of companies that will never sustain a Wikipedia article can legitimately maintain an accurate Wikidata item.

Both are earned, not bought, and both carry conflict-of-interest rules worth reading before editing anything about your own company. But they remain among the highest-leverage entity signals available. See Wikipedia and AI visibility and entity building.

YouTube: the multimodal source

As engines go multimodal, video — and its transcripts, titles, descriptions and chapters — increasingly feeds AI answers. YouTube is a rich source of how-tos, reviews and demonstrations, and the transcript is the part that matters most for text-based answer synthesis.

There is a concrete implication here that most brands miss: if your videos rely on auto-generated captions that mangle your product names, that is the version of your brand entering the corpus. Uploading accurate transcripts is a small, boring, high-return task. See YouTube and AI visibility.

Review sites: the trust layer

G2, Capterra, Trustpilot and their peers are where engines look to gauge sentiment and credibility, especially for software and services. Volume, recency and quality of reviews all feed how confidently an engine recommends you.

One nuance matters more than the rest: these platforms publish summaries — “users praise X but note Y as a limitation” — and a summary is far more extractable than a hundred individual reviews. That summary is often what propagates into answers. It is worth knowing what yours currently says, because it is effectively a paragraph of your positioning written by someone else. See review sites and AI visibility.

Q&A sites: the question-shaped corpus

Quora and similar sites contain content structured exactly the way users query AI — as questions and answers. That makes them both a source engines draw from and a map of how real people phrase things, which is useful input when you are deciding which prompts to track. See Quora and Q&A sites for AI visibility.

News and PR: the authority injection

Credible press coverage is one of the strongest authority signals available. When reputable publications describe your brand, that coverage propagates into training data and gets retrieved in live answers — it is one of the few channels that works on both clocks at once. Digital PR aimed at earning genuine mentions is a core AEO tactic, not just a brand-awareness play. See news and PR for AI visibility and PR strategy for AI visibility.

LinkedIn: the B2B credibility surface

For B2B brands, LinkedIn content and company presence contribute to how engines perceive expertise and authority. Thought leadership that earns genuine engagement becomes part of the corpus engines draw on. See LinkedIn and AI visibility.

Your own site is still necessary — just not sufficient

None of the above means deprioritizing your website. Your own pages are what engines reach for on branded and product-specific questions, they are where the canonical facts live, and every third-party source that describes you correctly is usually working from something you published.

The mundane prerequisite is that engines can actually fetch those pages. Crawler access is governed by robots.txt, standardised as RFC 9309, and the major AI crawlers document themselves publicly — OpenAI’s bots documentation and Perplexity’s crawler documentation both list user agents and behaviour. A disallow rule added during a site migration removes you from retrieval-based answers entirely and silently. Check it before you spend a quarter on content.

The unifying principle

Notice what these channels have in common: none of them is about gaming a system. They are about being genuinely present, credible and consistent across the places your category is actually discussed. The brands AI recommends are the brands the web already talks about favorably. Your job is to make sure that conversation exists, is accurate, and is well-sourced.

That reframes AEO from “optimize my website” to “shape my presence across the sources AI trusts.”

The limits of this map

Two honest caveats, because a channel list invites more confidence than the evidence supports.

Channel weight is not published and not stable. No provider discloses how heavily it weights Reddit versus Wikipedia versus a trade publication, and there is no reliable public figure for it. Anyone offering you percentages is inferring from small samples of visible citations — which, per the first section, only reflect the retrieval half of the mechanism. Treat any channel ranking as directional.

The right mix is category-specific. Review platforms dominate for software; Reddit dominates for consumer products with strong communities; trade press dominates in regulated industries where the general web has little to say about you. The list above is a starting hypothesis, not a plan.

Which is exactly why the first move is measurement rather than a channel campaign. Start by auditing which sources actually show up when engines answer questions in your category — with grounded scans, so the citations are real links rather than extracted domains — and then go earn your place in the ones that turn up.