How do I show up in AI search — ChatGPT, Gemini, Perplexity, Claude, Grok, DeepSeek?

The Ranking Factory checks how AI engines answer questions about your market and finds where they name competitors instead of you. Then it builds the missing evidence — clear answers, comparisons, useful tools, worked examples — publishes it, and re-measures on later runs whether you start getting named — which can take weeks, and which no tool controls. Results depend on your market and competition; no tool can promise citations or timescales.

Is this SEO, or something new?

Both. Traditional SEO works to rank your pages in a list of links. This is the newer "entity" or "AI-search" approach — sometimes called GEO — which works to make AI and Google understand your business well enough to include it in their answers. The Ranking Factory does the second, on top of the classic Google indexing it has always done.

Do you touch my website?

Never without you connecting it. By default everything is built off-site — inside Google's own properties (Drive, Cloud, Blogger, Business Profile) and your other connected channels — and your site is not modified. If you do connect a CMS like WordPress, we can publish gap-closing pages onto your own domain, which is the stronger placement: content on your own site carries more weight than anything hosted elsewhere. Either way, some fixes are on your site and only you can make them — we write them up and hand them over rather than pretending we can do it for you.

Where does the content you build actually end up?

Each subject gets several objects — a guide, a specification, a checklist, a data table, a deck — published to Google properties where they are individually indexable. Those then become ONE page on your own domain, because they are all about the same search and two pages of yours competing for it helps nobody. That page is assembled from what was already written rather than generated a second time, so it costs nothing extra and is not a reworded duplicate. The Evidence Engine lists any subject where the evidence exists and that page has not been made, and publishes it in a click — including a backlog we found across live accounts, where 493 of 505 subjects had evidence published and no page on the customer's own site. It will not offer a subject your own site already answers.

Is it safe and white-hat for client work?

Yes. No PBNs, no spam, no shortcuts. Every action is reversible, logged in your Timeline, and built inside Google-verified properties — so it's safe to run across all your clients.

What is the Evidence Engine?

It's the part that does the ongoing work: it finds where AI and search engines are missing you, builds the evidence they look for, publishes it, and then checks whether you start getting named — on a schedule, without you lifting a finger.

How much of this is automatic — what do I actually have to do?

Almost all of it runs on its own. You add your business and connect a Google account once; from then on the Evidence Engine finds your gaps, builds and publishes the missing evidence, and re-checks your visibility on a schedule — you just watch it work from your dashboard, with a link to everything it publishes. The only things it can't do for you are edits to your own website (it flags those with a ready-to-apply fix) and prefer your own keyword plan (you can hand it a list of keywords to cover instead of, or alongside, the gaps it finds).

Will this get me cited on Reddit, Wikipedia or review sites?

Not directly, and be wary of anything that claims otherwise. Most AI citations come from third-party sources — Reddit threads, Wikipedia, review platforms, industry press — and no tool can honestly manufacture those. What this does is make you the kind of business those answers can draw on: structured, evidenced, crawlable, with clear answers published where AI can read them. We show you which sources AI is actually citing for your market so you can go and earn a place in them. We will not post to Reddit or edit Wikipedia on your behalf — both prohibit it.

Why did my visibility change when I haven't done anything?

Because AI answers are not deterministic — ask the same question twice and you can get different businesses named. That is a property of the engines, not a fault in the measurement. It is why we report rates with a confidence range and the sample size behind them, why we treat trends across runs as the signal rather than any single run, and why adding a new engine starts a fresh trend line instead of quietly changing your history. If you want to see how much an answer moves, you can ask each prompt 3 or 5 times in one run: we then report stability — whether repeats agreed — separately from the rate itself, and list the exact prompts that changed their answer.

Do I need my own AI key, and what does it cost?

Yes, for the parts that ask the AI engines about you — those run on your key and spend your credit, which is why we never run them without you choosing to. Gemini has a free tier that works fine to start. The site audits need no key at all: they read your site directly, so you can see real findings before spending anything. Each engine you add is another engine you can see into, not a more accurate reading of the same one.

What can't you fix for me?

Anything that lives on your own website and needs a developer. The most common are content that only appears after JavaScript runs (Google renders it, most AI crawlers don't, so they see an empty page) and a CDN blocking AI crawlers even though your robots.txt allows them. We detect both and tell you exactly what to change — we just don't pretend there's a button for it. Everything we hand you is labelled: we fix it, paste this, or needs a developer.

How accurate are the numbers?

As accurate as the sample allows, and we show you the sample. Every rate carries a 95% confidence range and the number of prompt-and-engine pairs behind it; a wide range means few samples, not a poor result. You can ask each prompt three or five times and we report stability separately from the rate, and list the questions whose answer changed. Every figure says which engine API it was measured on — that is not the same as Google AI Overviews or the ChatGPT app, and we do not claim it is. Anything we could not measure is shown as unmeasured, never as a zero. The whole method is written up at /how-we-measure, including what the numbers do not claim.

Does it publish without asking me?

By default, yes — that is the point of an engine — and it tells you exactly what went out, item by item, with links, in the weekly report and on the project page. If you would rather approve first, turn on "review before publishing" in Settings: every post then waits for you and you are emailed the moment something is waiting. Either way nothing is published without grounding in your real content, no unsourced superlative survives the claim check, and nothing is announced until we have checked a stranger can open the link.

Which AI engines does it measure?

ChatGPT, Gemini, Perplexity, Claude, Grok and DeepSeek — whichever you connect, using your own keys. Where an engine can search the web as it answers (Gemini with Google Search grounding, Perplexity, and OpenAI's search tool on a GPT-5, GPT-4.1 or GPT-4o model) we say so and report what it read; where it can only answer from training, the result is labelled as such. Adding an engine widens the picture; it does not sharpen it, because precision comes from more prompts per engine, and we tell you that before you add one.

How quickly will I see results?

Evidence starts publishing from your first run, and you can watch your Entity Health and Timeline right away. How fast that turns into citations or rankings depends on your market and competition — we show you exactly what's happening as it happens, and we don't promise positions or timescales no tool can control.

Earned Media Drives Generative Engine Optimization (GEO)

Earned media drives 84% of AI citations. Discover how generative engine optimization is reshaping search strategy and driving brand visibility in AI search.

Search engine optimization is undergoing a fundamental structural shift. For over two decades, digital marketing strategies revolved around getting web pages ranked on the first page of traditional search engine results pages (SERPs). Today, millions of users ask questions directly to AI engines—such as Perplexity, ChatGPT, and Google's AI Overviews—and expect direct, synthesized answers rather than a list of links.

This shift requires a new discipline: Generative Engine Optimization (GEO). Unlike traditional SEO, which focuses on domain authority and keyword density to capture clicks, GEO focuses on ensuring that large language models (LLMs) understand, recommend, and explicitly cite your business when generating answers. Crucially, research published by FinancialContent in Best LLM SEO Agencies See 84% of AI Citations Driven by Earned Media, Paid at 0.3% reveals that 84% of AI citations originate from independent third-party coverage—earned media—while paid placements account for a mere fraction of a percent.

To succeed in this landscape, organizations must adapt their technical hygiene and PR efforts around the mechanics of how generative engines retrieve and ground their information.

Understanding the Mechanism: How AI Engines Select and Cite Sources

To optimize for generative engines, one must first understand how an AI search engine constructs an answer. Generative engines do not evaluate web pages using traditional crawlers and PageRank algorithms alone. Instead, they rely on a process called Retrieval-Augmented Generation (RAG).

The RAG pipeline works through four distinct mechanical steps:

Query Tokenization and Embedding: When a user submits a natural language query, the system converts the input text into a numerical vector—a high-dimensional representation of semantic intent.
Vector Index Retrieval: The engine queries its index (or real-time web search index) to find document snippets whose semantic vectors closely align with the user's prompt.
Context Window Construction: The retrieved snippets are passed into the LLM's prompt window as reference context, providing the factual foundation for the generated answer.
Response Synthesis and Citation Grounding: The LLM synthesizes a coherent answer based on the context snippets. To minimize factual errors and hallucinations, the model attaches explicit source citations directly to the sentences derived from those snippets.

As explained by PRINT Magazine's analysis of What SEO, AEO, and GEO Really Mean Now, and What Smart Businesses Should do Next, generative engine optimization targets this specific retrieval phase. If your brand lacks semantic presence across the documents indexed during retrieval, the LLM will neither name your company in its response nor cite your web assets as a source.

Why Earned Media Drives 84% of AI Citations

Why does earned media dominate AI citations while brand-owned websites and paid channels lag behind? The answer lies in how LLM alignment algorithms filter for entity consensus and factual trustworthiness.

When an engine performs retrieval, it encounters conflicting or overlapping claims. To determine which entities to recommend, LLM architectures prioritize cross-referencing and third-party corroboration. A statement made on a brand's official website is classified as a single-source self-claim. However, when independent news publications, trade journals, and industry blogs publish articles discussing that brand, they create multiple independent data points across the web's vector space.

According to NewsVoir's report on Generative Engine Optimization in PR for AI Search, earned media provides the exact un-biased unstructured data that LLMs rely on to verify facts. When multiple reputable sources mention a business alongside specific industry solutions or product capabilities, the generative engine develops high statistical confidence in that relationship. Consequently, the RAG framework selects those third-party articles as context snippets, resulting in your business being named in the answer and the third-party publication—or your brand—being cited as the supporting source.

Furthermore, as detailed in Deloitte's guidance on How brand discovery has changed and what you must do now, consumer discovery has migrated away from linear directory searches toward multi-source algorithmic recommendations. Search strategies that rely strictly on owned assets miss the broader ecosystem of third-party coverage that AI systems ingest during real-time retrieval.

Identifying and Closing the "Evidence Gap"

When an AI engine fails to cite or recommend a business for a relevant query, it is rarely a technical crawl issue. Instead, it indicates an evidence gap: a scenario where the AI engine searches for candidates to satisfy a detailed query but cannot find sufficient independent, corroborated evidence linking the brand to those specific capabilities.

For example, if a user prompts an AI engine for "top enterprise CRM solutions for mid-market manufacturing," the LLM evaluates candidates based on available context. If your enterprise software has excellent owned documentation but zero trade press coverage connecting it to manufacturing workflows, the retrieval model will pass over your brand in favor of competitors with established third-party mentions.

Step-by-step Process to Audit and Close Evidence Gaps

Organizations can diagnose and close their evidence gaps without proprietary tooling by executing the following manual workflow:

Map Core Query Vectors: Identify 10 to 20 natural language prompts potential customers ask generative engines regarding your product category or service niche.
Audit Model Output and Sources: Input these prompts into major generative engines (such as Perplexity or Google AI Overviews). Record whether your brand is named in the response text and document every third-party domain cited in the footnotes.
Isolate Missing Entities and Attributes: Compare the cited articles against your brand's media footprint. Identify which specific facts, feature descriptions, or industry use cases are documented for competitors but absent for your business.
Execute Targeted Earned Media Outreach: Conduct digital PR campaigns, contribute expert commentary, and distribute technical research to the exact industry publications and trade journals that current generative search engines frequently retrieve for those prompt categories.
Verify Citation Recovery: Re-run the prompt set after external coverage is indexed to confirm whether the engine now incorporates your brand into synthesized responses.

To streamline this process at scale, automated platforms can systematically monitor these outputs. For instance, platforms like The Ranking Factory evaluate whether AI engines name and cite a business across target queries, automatically identifying the missing third-party evidence needed to trigger inclusion in subsequent retrieval cycles.

Combining Technical Hygiene with Strategic Media Relations

While earned media provides the factual backing required for AI citations, technical website structure ensures that generative engines can accurately associate external mentions with your official domain. As noted in Kantar's breakdown of From SEO to GEO: How technical hygiene became a growth lever, technical optimization remains a foundation for AI visibility.

To maximize the impact of your generative engine optimization earned media strategy, align technical hygiene with external outreach:

1. Implement Explicit Schema Entity Markup

Use Schema.org JSON-LD structured data on your website—specifically Organization, Product, and SameAs properties. The SameAs property explicitly links your domain to your official social profiles, Wikipedia entries, and major media coverage. This helps the LLM's entity resolution algorithms connect third-party earned media mentions directly to your primary website entity.

2. Align Technical Copy with PR Messaging

Ensure that the factual claims, statistics, and product definitions published in press releases and trade articles match the structured data and on-page messaging on your owned site. Factual consistency across internal and external assets increases the vector density surrounding your brand entity, making retrieval more likely.

As documented by Midwest Medical Edition in Google Weighs In on GEO. Here's What Changed, and What Didn't., core principles of clarity, structured authority, and technical accessibility remain critical even as search engines evolve from blue links to generative interfaces.

To verify that technical updates and earned media placements produce measurable results in AI engines, tools within The Ranking Factory can perform follow-up query runs to confirm whether an engine's citation frequency and brand attribution have improved over time.

Measuring Success in Generative Search

Traditional search metrics like organic keyword rankings and web session counts do not accurately capture performance in an AI-driven environment. A user may read a synthesized answer, receive a brand recommendation, and make a purchase decision without ever clicking a traditional search link.

To measure the impact of your GEO strategy, monitor the following metrics:

Share of Model (SoM): The percentage of queries within a given topic cluster where an AI engine explicitly names your brand in its generated output.
Citation Frequency: The number of times your owned domain or your earned media placements appear as hyperlinked citation sources in synthesized answers.
Attribute Co-occurrence: The frequency with which key brand attributes (e.g., "most secure," "enterprise-grade," "scalable") appear in close semantic proximity to your brand name in AI outputs.

As highlighted in Exploding Topics' list of The 8 Best AI Mode Tracking Tools for SEO and GEO in 2026, tracking these generative metrics allows organizations to prioritize PR and content investments that directly drive model inclusion.

Reframing Your Search Strategy for the Generative Era

The rise of AI search does not render search strategy obsolete, but it fundamentally shifts the focus from optimizing web pages for crawlers to establishing entity authority for language models. Because LLMs rely on Retrieval-Augmented Generation and consensus validation to answer user queries, earned media has become the primary driver of visibility, accounting for 84% of AI citations.

By identifying evidence gaps, securing strategic coverage in industry publications, and maintaining rigorous technical entity hygiene, organizations can ensure they are consistently retrieved, recommended, and cited across all major AI search engines.

To learn more about implementing structured frameworks for measuring and improving your brand's AI search visibility, explore our resources on The Ranking Factory Help Center.