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.

Generative Engine Optimization PR: Why Digital PR Wins AI

With earned media driving 84% of AI citations, Generative Engine Optimization PR is essential. Learn why digital PR is the new GEO strategy for AI search engines.

Traditional Search Engine Optimization (SEO) was built on getting web pages indexed and ranked within a list of blue hyperlinked results. Today, generative AI platforms—such as Perplexity, ChatGPT Search, and Google Gemini—do not present list-based indexes; they generate synthesized, natural-language answers directly within the chat interface. To earn visibility in these generative outputs, businesses must implement Generative Engine Optimization (GEO), the practice of structuring an entity's online presence so Large Language Models (LLMs) can reliably retrieve, verify, and cite its content.

The mechanics behind generative search differ fundamentally from traditional crawler-based search engines. An analysis covered by Best LLM SEO Agencies See 84% of AI Citations Driven by Earned Media, Paid at 0.3% reveals that 84% of citations in AI search engines originate from earned media publications, whereas paid visibility accounts for just 0.3%. Consequently, Generative Engine Optimization PR—the strategic placement of brand entities in independent, third-party publications—has become the primary driver of AI search discovery.

The Retrieval Engine: How AI Search Selects Citations

To understand why earned media commands such a high proportion of AI citations, it is necessary to examine how generative engines construct responses. Unlike a standard search index that ranks pages based on page-level backlinks and keyword matching, a generative search engine utilizes a multi-step architecture called Retrieval-Augmented Generation (RAG).

When a user prompts a generative engine, the software executes a systematic, four-part retrieval sequence:

Query Tokenization and Entity Extraction: The engine breaks the user prompt into numerical vectors (mathematical representations of semantic meaning) and isolates target entities, such as brand names, geographic regions, or product classifications.
Real-Time Vector Retrieval: The system queries its search index to locate web documents whose vector embeddings closely align with the user's request context.
Source Validation and Triangulation: The engine scores retrieved documents based on domain authority, contextual freshness, and third-party entity co-occurrence. It searches for consensus across multiple independent sources to ensure factual accuracy.
Generative Synthesis and Footnoting: The LLM synthesizes the extracted context into a single narrative answer, attaching inline footnoted links (citations) back to the primary web documents that provided the underlying facts.

As explored in What SEO, AEO, and GEO Really Mean Now, and What Smart Businesses Should do Next, transitioning from traditional keyword density to contextual entity validation alters how brand content must be distributed. If an LLM cannot verify a claim through external consensus across third-party domains, it will omit the brand from the synthesized answer or retrieve a competitor that possesses stronger external validation.

Why Earned Media Drives 84% of Generative Mentions

Earned media encompasses editorial features, trade press coverage, industry news articles, and independent expert reviews. In a RAG environment, third-party press provides the specific validation signals LLMs require to minimize hallucination—the tendency for an AI model to generate incorrect or unverified statements.

When an LLM evaluates a brand's self-published website, it classifies the information as high authority for primary company data (such as product specs or pricing), but low authority for qualitative claims (such as being the "leading" or "top-rated" solution). Conversely, when an independent journalistic source or trade publication mentions a company alongside specific attributes, the RAG framework registers this co-occurrence as an objective fact.

According to research published by Generative Engine Optimization in PR for AI Search, structuring digital PR specifically for AI discovery gives brands the corroborating external footprint required to pass the LLM's validation threshold. Securing consistent coverage across external media sources directly results in being named inside generative answers and receiving footnoted source citations in the engine output.

Implementing a Generative Engine Optimization PR Strategy

To execute a PR strategy designed for AI retrieval, marketing teams must align media outreach with the semantic ingestion patterns of LLMs. Practitioners can execute several actionable strategies without proprietary tooling:

1. Target Niche Trade Publications for Semantic Co-Occurrence

Generative search engines prioritize contextually relevant sources over broad, non-specific news sites. Securing editorial coverage in specialized trade portals creates strong semantic links between your brand entity and target industry terms.

Example: If a company offers corporate cybersecurity software, getting featured in a cybersecurity trade publication with the phrasing "Company X, an enterprise endpoint encryption platform" trains the vector space of AI models to pair that brand entity with the concept of endpoint encryption. As GEO vs SEO: Is AI Search Really a New Discipline? points out, foundational public relations techniques gain technical importance when adapted to feed structured information to AI models.

2. Publish Data-Driven Research to Earn Source Footnotes

LLMs frequently cite original statistical data when answering user queries about market trends, benchmarks, or industry standards. By conducting proprietary surveys, benchmark studies, or data reports, businesses create citeable assets that journalists reference.

When journalists link to your research report as a primary source, generative search engines retrieve those news articles, track the original data back to your publication, and insert a direct footnote citation pointing to your website. In How brand discovery has changed and what you must do now, the shift toward decentralization in digital discovery underscores the necessity of establishing clear, referenceable data points across the web.

3. Maintain Strict External Entity Hygiene

Discrepancies in corporate information across news releases, media profiles, and editorial features create entity ambiguity. If an AI engine encounters conflicting product names, founding dates, or service descriptions across external media, its confidence score drops, reducing the probability that it will name the business in a generated response.

As documented in From SEO to GEO: How technical hygiene became a growth lever, expanding technical hygiene to include external entity consistency ensures that LLMs process unified data, leading to higher retrieval precision during live engine runs.

Measuring AI Visibility and Evidence Gaps

Tracking generative engine performance requires evaluating two distinct metrics across dynamic prompt outputs:

Mention Rate: The percentage of target prompts where the AI engine explicitly names the business within its generated text.
Citation Rate: The frequency with which the engine includes a hyperlinked footnote directing users to the brand's primary site or earned media articles.

When an AI engine answers a relevant commercial query (e.g., "What are the top enterprise inventory management systems?") but fails to list your company, an evidence gap exists. An evidence gap occurs when the engine's real-time retrieval layer fails to find sufficient third-party consensus to include your brand in its summary.

Because generative engine outputs fluctuate based on prompt variations and real-time retrieval updates, auditing these outputs manually across platforms like ChatGPT, Claude, and Perplexity is unfeasible at scale. Industry frameworks for monitoring these outputs are covered by The 8 Best AI Mode Tracking Tools for SEO and GEO in 2026.

To streamline this process, platforms like The Ranking Factory measure whether AI engines name and cite a business across high-intent category prompts. When an omission occurs, the platform identifies missing evidence—such as key media mentions or unindexed press coverage—so PR teams can target the exact publications required to establish third-party consensus. Once new earned media is published, the platform re-evaluates the subsequent engine run to verify if citation rates improved.

For detailed guides on setting up structured AI citation measurement workflows, visit our help documentation page.

Securing Your Brand in Generative Search Output

The transition from traditional web search to generative search requires a parallel shift in digital marketing execution. Because generative search engines rely on Retrieval-Augmented Generation to construct responses, brand visibility is dictated by third-party web trust. With earned media driving 84% of AI search citations, Digital PR has evolved from a secondary awareness lever into the cornerstone of Generative Engine Optimization PR. By securing authoritative trade coverage, maintaining entity consistency, and systematically addressing evidence gaps, brands ensure they remain visible, named, and cited in the AI-driven search ecosystem.