How LLMs Index the Web: What AI Search Actually Uses

A Reddit SEO debate framed the issue as a coming battle between Google’s index and AI-owned alternatives such as OpenCrawl. The more useful answer is less dramatic: how LLMs index the web depends on the engine, the prompt, and whether the system is answering from learned knowledge or retrieving live sources.

For marketers, the payoff is practical. Instead of guessing that a strong Google ranking guarantees visibility in ChatGPT, Gemini, Perplexity, Claude, Microsoft Copilot, or Grok, we can identify the discovery path behind an answer and measure whether our brand is actually mentioned, cited, or displaced by a competitor.

The short answer: LLMs do not use one universal web index

Some AI systems operate proprietary web crawlers. OpenAI documents separate crawlers including GPTBot and OAI-SearchBot; Perplexity documents crawlers that gather and index web information; Anthropic states that ClaudeBot obtains public-web training data. Those facts confirm that major AI providers collect web content themselves, but they do not prove that every answer comes from a single, Google-style provider-owned index. (developers.openai.com)

A modern AI answer can be informed by four different systems at once:

1. Pretraining knowledge — patterns learned from large datasets before a model is deployed.
2. Provider-owned crawling — a bot such as OAI-SearchBot, ClaudeBot, or PerplexityBot fetching public pages.
3. Third-party search infrastructure — for example, Google Search or Bing Search supplying fresh results.
4. Live retrieval and generation — a model selecting sources, reading passages, and composing a response through retrieval-augmented generation (RAG).

That is why the binary question—“Do LLMs have their own index?”—does not take us far enough. A provider can crawl the web, use an external search partner, maintain cached documents, and still answer some prompts without live retrieval.

The original Reddit thread is valuable because it captures a real operational concern: SEO teams are often watching Google Search Console while AI engines are using different crawlers, retrieval policies, and answer-generation logic. But the claim that a named in-house index, such as “Labrador,” explains all OpenAI behavior should be treated as unverified unless OpenAI publicly documents it. The public evidence supports a mixed model, not a complete map of any provider’s private architecture. (developers.openai.com)

How LLMs index the web: six terms marketers should separate

The word “index” gets used too loosely in AI search discussions. We separate six concepts because each one affects a brand’s chance of appearing in an AI-generated answer.

1. Crawling

A crawler fetches pages from the public web. Googlebot crawls to support Google’s search indexes, while OpenAI, Perplexity, and Anthropic each document AI-related crawlers. Crawling means a bot may have visited a page; it does not guarantee ranking, retrieval, citation, or a brand mention. (developers.google.com)

2. Indexing

An index is a structured corpus that makes documents retrievable. Google’s search index is built to return ranked documents and rich search results. A provider may also build searchable stores from crawled pages, but public documentation rarely reveals their coverage, refresh timing, duplicate handling, or ranking weights.

3. Model training

Training is different from indexing. During pretraining, a model learns statistical relationships from large volumes of data; it is not simply storing a browsable copy of every source. A model may know that a brand exists without being able to show a current source, and it can generate an outdated answer if no live retrieval is used.

4. Retrieval-augmented generation

RAG is the process of retrieving external content at answer time and giving that material to the model as context. Google’s Gemini API describes grounding with Google Search as a way to connect a model to real-time web content and provide citations beyond the model’s knowledge cutoff. OpenAI’s web-search documentation similarly describes models searching the internet before producing sourced answers. (ai.google.dev)

5. Ranking

Ranking decides which documents or passages are selected for a query. Google ranks pages in its index. An LLM system may first rank web results, then rerank passages, then decide which sources are sufficiently useful to cite. A first-place Google result can therefore be absent from an AI answer, while a lower-ranking but more directly relevant source is used.

6. Answer generation, mentions, and citations

Finally, the model generates prose. A mention means the brand appears in the answer. A citation means the system visibly attributes a claim or recommendation to a source. They overlap, but they are not equivalent: a brand can be named without a link, or a page can be cited without its company being recommended.

What ChatGPT, Gemini, Copilot, Perplexity, and Claude publicly reveal

The public evidence varies by provider. We should avoid assuming that an API feature precisely mirrors every consumer-product response, but the published documentation shows the retrieval ingredients available across major AI engines.

Microsoft’s documentation is unusually clear about the distinction between model knowledge and fresh web grounding: Bing Search can fill information gaps with real-time public web data. Perplexity likewise exposes a Search API with real-time ranked results and an Agent API for web-grounded answers with citations. (learn.microsoft.com)

This does not mean every ChatGPT answer uses OpenAI crawling, every Gemini answer uses Google Search, or every Claude answer uses ClaudeBot. It means the engines have multiple content-acquisition paths, and the path used can change by product, query type, geography, account tier, tool setting, and time.

LLM search versus Google Search: retrieval is only half the system

Google Search is fundamentally a document-retrieval and ranking system. It crawls, indexes, ranks, and presents pages, while Google Search Console helps site owners inspect some of that process. Google says crawling and indexing can take days to weeks after a page is added or changed, and even requesting a crawl does not guarantee inclusion. (developers.google.com)

An LLM search system has a different final objective: produce a helpful answer. It may use a search engine to obtain candidates, but it then performs another layer of selection and synthesis.

A simplified comparison looks like this:

• Google Search: query → indexed documents → ranking → search result list.
• AI search with RAG: query → query interpretation → search or retrieval → passage selection → model-generated answer → optional citations.

That difference changes what “winning” means. In Google, a brand might target a top-three organic position for “best payroll software.” In Perplexity or ChatGPT, the more relevant question is whether the engine recommends the brand in the answer, names it in the shortlist, attributes a claim to its site, or cites a competitor instead.

This is also why traditional rank tracking alone cannot explain AI visibility. A page can rank well but be omitted because the answer needs a concise comparison, current pricing, third-party validation, local availability, or a source with clearer entity information. Conversely, an authoritative review, directory profile, or news article can influence the answer even when the brand’s own page is not the cited source.

For a deeper framework for the metrics behind this distinction, see our AI search measurement guide.

Can an AI answer cite a page before Google indexes it?

Yes, it is possible—but a single observation does not prove an AI provider has a superior or completely separate web index.

The invented-term test discussed in the ranking debate is useful as a diagnostic. Publish a new, uniquely named page or phrase, verify whether it appears in Google, then test equivalent prompts across ChatGPT, Gemini, Copilot, Perplexity, and Claude. If an AI engine surfaces it before Google shows it, several explanations remain plausible:

• the provider’s crawler discovered the page first;
• the engine used Bing or another partner with faster discovery for that URL;
• the page was already available in a cache or third-party dataset;
• the engine followed a direct URL or navigated from a known source;
• the Google test was incomplete because indexing varies by query, location, and data center.

Google itself cautions that crawl and indexing timing cannot be guaranteed. That makes a “page appeared in AI before Google” test interesting evidence, not a complete reverse-engineering result. (developers.google.com)

To make the experiment more credible, use at least 10 to 20 pages across several domains, log publication times, block no important crawlers, record bot traffic, repeat prompts over multiple days, and capture exact citations. One invented term and one response from one model can reveal behavior; it cannot establish a provider’s full architecture.

The OpenCrawl and Common Crawl debate needs more precision

The Reddit post raises OpenCrawl as an alternative web-data layer. “OpenCrawl” can refer to different open-source crawling projects, so we should not treat the name alone as evidence that a particular AI provider uses it. Publicly verifiable documentation is much stronger for named provider bots such as OAI-SearchBot, ClaudeBot, and Perplexity crawlers. (developers.openai.com)

Common Crawl is the better-documented example of open web infrastructure. The nonprofit maintains an open repository with more than 300 billion pages spanning 19 years, making it a major resource for researchers and developers working with web-scale data. (commoncrawl.org)

However, an open crawl is not automatically a high-quality search index. A production AI search system still needs to handle:

• spam, duplicates, and low-quality pages;
• recency and rapidly changing content;
• entity disambiguation for brands with similar names;
• local and language relevance;
• source authority, licensing, and safety;
• query-specific ranking and citation selection.

Google and Bing have spent decades building discovery, index maintenance, ranking, and anti-spam systems. An AI provider can reduce dependence on them by operating crawlers and retrieval systems, but that does not eliminate the quality problem. It simply moves the problem into a different stack.

SEO for LLMs means improving eligibility, evidence, and entity clarity

“SEO for LLMs” is not a replacement for technical SEO. It is a practical extension of it: make your content crawlable, understandable, current, corroborated, and useful enough to be selected when an AI engine assembles an answer.

We recommend focusing on six durable actions:

1. Keep critical pages technically accessible. Use clean status codes, indexable HTML, sensible internal links, updated XML sitemaps, and robots.txt rules that match your business choices. Check whether relevant bots are reaching the site in server logs.
2. Allow the bots you intend to serve. OpenAI distinguishes OAI-SearchBot from GPTBot, and those permissions are independent. Decide deliberately whether you want search discovery, training access, both, or neither. (developers.openai.com)
3. Write answer-ready pages. State who the product is for, what it does, where it works, what it costs when pricing is public, and how it differs from alternatives. Clear tables, definitions, proof points, and dated updates make retrieval easier.
4. Build entity consistency. Use the same brand name, product names, category language, founders, locations, and official profiles across your site and credible third-party sources.
5. Publish evidence beyond your own claims. Reviews, analyst coverage, partner pages, reputable comparisons, original research, and customer stories can all influence which brands an AI engine associates with a buyer question.
6. Protect conventional search performance. Gemini’s Google Search grounding and Copilot’s Bing-grounded web search show why Google and Bing visibility remain part of AI visibility—not obsolete alternatives to it. (ai.google.dev)

Our comparison of SEO, GEO, AEO, and AIO explains where those labels overlap. The important work is not adopting a new acronym; it is improving the inputs AI systems can retrieve and trust.

Measure AI visibility at the prompt level, not from assumptions

A brand does not have one fixed “AI ranking.” It has visibility across specific prompts, engines, locations, dates, and answer formats.

For example, a B2B cybersecurity company may appear in ChatGPT for “best endpoint detection tools for a 500-person company,” be absent from Gemini’s equivalent answer, and be cited by Perplexity only through a third-party review. Treating these as one score hides the action needed.

A useful measurement workflow includes five steps:

1. Build a buyer-question set. Include category prompts, comparison prompts, alternatives, use cases, industry-specific questions, and local intent where relevant. Start with 50 prompts for a focused category rather than 500 vague keywords.
2. Run the same prompts across engines. Track ChatGPT, Gemini, Perplexity, Claude, Microsoft Copilot, and Grok where those engines matter to your audience.
3. Capture answer-level data. Record whether your brand is mentioned, its position in a list, sentiment or recommendation context, citations, cited domains, and competitors named instead.
4. Calculate share of answer. If a brand is mentioned in 18 of 60 tracked prompt-engine responses, its raw mention share is 30%. Add weighted measures for first recommendation, citation, and high-intent prompts.
5. Turn gaps into content and authority work. If a competitor appears for “best tools for agencies” because review sites describe its agency workflow, improve the corresponding evidence—not just a generic product page.

AI Visibility Tracker is built for that job: a local-first desktop workflow that uses your own API keys to test real prompts, identify competitor gaps, and measure mentions, citations, and share of answer across major AI engines. That gives us repeatable evidence rather than a screenshot-driven theory of AI search.

For teams moving from rank reports to competitive answer analysis, read our guide to AI search competitor analysis versus SEO benchmarking.

What we still do not know—and how to work responsibly

No outside observer has a complete map of ChatGPT, Gemini, Copilot, Perplexity, Claude, or Grok retrieval pipelines. Providers can change crawlers, search partners, models, ranking policies, citation behavior, and product defaults without exposing every implementation detail.

That uncertainty matters because it discourages overconfident conclusions such as “Google no longer matters” or “this provider uses only its own index.” The public record supports more modest conclusions:

• AI providers do operate or document proprietary crawlers.
• Several major systems can access live web results through Google Search, Bing, or proprietary search tools.
• A model’s learned knowledge and its live retrieval behavior are not the same mechanism.
• Citation patterns are observable, even when underlying index architecture is not.

The best response is empirical. Log crawler activity, maintain strong technical SEO, test high-value prompts across engines, compare competitors, and repeat the measurement over time. That is how we future-proof SEO for ChatGPT and Perplexity without treating every AI-search rumor as a strategy.

FAQ

Are LLMs building their own search or web index?

Some are clearly building parts of their own web-data infrastructure. OpenAI, Perplexity, and Anthropic publicly document crawlers, while Google and Microsoft operate established search systems. But a crawler does not prove a complete, standalone index powers every answer. AI products can combine learned knowledge, proprietary crawls, cached content, partner search results, and live retrieval. (developers.openai.com)

Do LLMs use Google Search or their own crawlers to find web content?

Both can be true. Gemini’s grounding tools connect models to Google Search, while Microsoft documents Bing-powered web grounding for Copilot. OpenAI, Perplexity, and Anthropic also document their own crawler activity. The retrieval path can vary by product and prompt, so marketers should not assume a single source of web content across all AI engines. (ai.google.dev)

How is an LLM search system different from Google’s index and ranking process?

Google primarily returns ranked documents from its index. An LLM search system may retrieve ranked documents or passages, then synthesize them into a direct answer. That extra generation layer means source ranking, citation selection, and brand mentions can diverge from traditional organic positions. A page ranking first in Google is not automatically the source an AI engine chooses to cite.

Can a page appear in an AI-generated answer before Google indexes it?

It can happen, especially if an AI system or its search partner discovers the page through a different crawl, cache, or retrieval path. But one test cannot prove an independent AI index because Google indexing timing varies and Google does not guarantee a fixed crawl schedule. Use repeated, controlled tests and logs rather than drawing conclusions from one new page. (developers.google.com)

How can marketers measure whether AI engines mention their brand?

Track a fixed set of real buyer prompts across ChatGPT, Gemini, Perplexity, Claude, Copilot, and other relevant engines. For each response, capture brand mention, citation, answer position, competitors named, and source domains. Then calculate prompt-level visibility and share of answer over time. This reveals gaps that Google rankings alone cannot show.