Work on the information available to retrieval

SEO for Large Language Models

LLM SEO addresses web information that a large language model may encounter while browsing, retrieving sources, or generating a source-backed answer. The work can improve page access, factual clarity, entity consistency, and corroborating evidence. Model weights and the final response remain under the control of the third-party platform.
SEO for Large Language Models service artwork

Some of the 500+ brands we've worked with

See all references
  • Enerjisa
  • Ülker
  • Little Caesars
  • Peak Games
  • Sporjinal
  • Wall Street English
  • Amazon
  • BMW
  • Shell
  • Hyundai
  • PepsiCo
  • Red Bull
  • Decathlon
  • MediaMarkt
  • Bayer
  • Sanofi
  • EY
  • KPMG
  • GE
  • 3M
  • Domino’s
  • Lexus
  • Trendyol
  • Hepsiburada

Inputs your team can examine

The useful work happens before the model makes its choice

Crawler access, passages, entities, and source support can all be improved and tested. The platform owns model behavior, so none of this work can cross into changing weights or controlling an answer.
GEO Strategy & AI Search Audit service artwork

GEO Strategy & AI Search Audit

Technical AI Search Optimization service artwork

Technical AI Search Optimization

We compare bot access and retrieval paths with the rendered page. The record shows what each system receives and whether key information survives extraction.
AI Citation Content Optimization service artwork

AI Citation Content Optimization

Retrieval may select only a fragment. A self-contained passage keeps the subject, supported claims, context, and necessary qualifications in that fragment.
Entity & Knowledge Graph Optimization service artwork

Entity & Knowledge Graph Optimization

Conflicting versions of a brand or product introduce ambiguity. We reconcile names, attributes, and relationships across records a system may find.
AI Search Authority & Citation Strategy service artwork

AI Search Authority & Citation Strategy

Independent sources can corroborate claims on your site. Mapping them reveals where outside evidence is absent, unclear, or too weak to support the point.
AI Visibility Monitoring & Answer Accuracy service artwork

AI Visibility Monitoring & Answer Accuracy

Information available at retrieval time

LLM SEO works with retrieval-time information

When an LLM browses or retrieves web content, it receives whatever pages and sources that system can access at the time. LLM SEO improves the accessibility, quality, and consistency of those inputs.

The GEO path into the model layer

Access, extraction, identity, then corroboration

The path begins with crawler access, then moves through extracted passages, entity understanding, and corroborating sources. GEO can work on each of those conditions. Training choices, hidden retrieval rules, and final answer generation stay with the platform.

Scope and ownership

We work on the retrieval-time inputs; the model layer is not ours to move

The label is useful because it marks where your influence over web information ends.

We improve the accessibility, clarity, and consistency of the pages and sources an LLM can reach at retrieval time, working through crawler access, extracted passages, entity understanding, and corroborating evidence in that order. A live page being browsed or retrieved does not change a system's trained parameters, so the test follows the information path actually in use; a retrieved fragment keeps its subject, source relationships, and necessary qualifications so it stays true on its own. Training choices, hidden retrieval rules, and final answer generation remain with the third-party platform, and observed outputs cannot reveal — much less place under your control — every internal ranking decision.

Separate what you can observe from what you cannot control

The LLM SEO label helps technical and product teams locate the point where their web information meets the model layer. It also makes the end of their influence explicit.

  1. Current retrieval does not rewrite training

    A system may browse or retrieve a live page without changing its trained parameters. The test must follow the information path that is actually in use.

  2. A fragment may carry the whole answer

    Its subject and source relationships need to remain explicit. Necessary qualifications stay with the fragment as well.

  3. Independent evidence adds corroboration

    Consistent facts across owned and external sources give the system more evidence to assess than one self-published claim.

  4. Hidden ranking decisions remain hidden

    We can test outputs and exposed sources. Those observations cannot reveal every internal ranking or generation decision, much less place it under your control.

Clients on the work

What it is like to work with Zeo

Our clients describe the work in their own words.

  • Didem Namver

    We were working with a global supplier on SEO before. Accessibility and process management were a little more difficult with these teams. At the same time, in terms of budget, global suppliers were more costly for us due to exchange rate differences and man-hours. When we started working with Zeo, we first went through an audit, fixing the results and problems. After these processes, we placed SEO in a strategic place for digital marketing and created an always-on SEO strategy for our brands and implemented it step by step.

    Didem Namver, Sr. Head of Digital
  • Yiğit Ertem

    As MediaMarkt, we have been working with Zeo for 6 years in a very tight and coordinated way to manage our SEO processes. We receive the highest level of feedback from Zeo on increasing and improving our organic traffic. It is very enjoyable to get fast support on all our issues 24/7 and to work with Zeo to better embrace and continuously improve our brand, and we recommend it to everyone.

    Yiğit Ertem, Ecommerce Web Channel & PIM Department Manager
  • Emre Baykal

    We strictly follow the regulations and algorithms that can change at any time; We need to act quickly. At this point, Zeo has become a partner that meets all our expectations with its professional, innovative and solution-oriented approach. The Zeo team has become a stakeholder in success by looking at our optimization processes and our brand as their own value. As the Acıbadem Healthcare Group family, we would like to thank all the Zeo team, who work tirelessly, constantly improve, and do not compromise on keeping their energy high under all circumstances!

    Emre Baykal, Director of Digital Marketing
  • Kaan Deniz
    Jack Martin

    Besides increasing our website's visibility in the UK with Zeo, the experienced team that closely follows its work and has extensive experience in its field has always made us feel like we are in the right place for SEO. We are on the right track with Zeo in a country and industry where competition is high.

    Kaan Deniz, Founder
  • Zeynep Yaşar
    Armağan

    We talked to a number of agencies while we were planning our website's infrastructure migration, and we ended up with Zeo. They start by understanding the problem, then bring fresh approaches to solving it better. Communication is strong, and they support you at whatever point you turn out to need it. Every minute we spend working with them convinces us further that we made the right call. If you're looking for someone to sort out the digital side, you're in the right place.

    Zeynep Yaşar, Business Development Specialist & E-commerce Project Manager

Adjacent search evidence

Case Studies

Organic search engagements establishing the indexation, content depth, and domain authority that AI answer engines draw from.

Allianz Türkiye

SEO Visibility: 56%+, Organic Sessions: 1.7M+

Lexus Türkiye

100% Increase in Organic Sessions!

Albaraka Türk

Total organic impressions: 120M+

People who watch how AI cites a brand

GEO work starts with recording what AI answers actually say about a brand today, then moves to the parts you can influence. The consultants below work on the specific capability this page covers.

Content we've produced on this topic

Articleby Can MutioğluHow Should GEO-Focused Content Writing Be?Articleby Didem HimmetliThe Transformational LLMs on Search and RecommendationsArticleby Gülşah Şahin ÖzkanSearch Everywhere Optimization (SEvO): What It Is and How Search Behavior Has Changed

Tools we use

What we watch AI answers with

Generative search leaves less to read than a rankings report does, so most of this work is assembling evidence from tools that were never built for it.

AI answer and citation tracking

  • OpenAIThis page promises to follow information from crawler access to retrieval and then show exactly where a client's influence stops. Running the question set directly against ChatGPT is how that boundary gets demonstrated rather than described: the same question with browsing on and off produces different behavior, and the page is explicit that you cannot place content in a model's training set or edit its weights.
  • OpenRouterThis page measures with a versioned set of prompts, markets, and platforms and reports only what appeared in the observable sample. OpenRouter lets the same prompt run against several models through one interface, which keeps the comparison honest about what changed: when a passage performs differently across providers, the difference belongs to the model, not to a variation in how the request was made.
  • AhrefsSince this page is explicit that hidden ranking decisions stay hidden, work here can only be judged by its downstream effect; Ahrefs Brand Radar's citation tracking is what gets checked afterward to see whether improved access and corroboration moved anything, rather than promising the mechanism itself.

Entity and structured data

  • Schema AppThis page's fourth step, independent evidence adds corroboration, is a role Schema App's structured entity data plays directly: markup that states a fact in machine-readable form alongside the prose gives a retrieval system a second, independently checkable version of the same claim.
  • InLinksBecause retrieval selects a limited section, this page requires that section to carry its own subject and claim. InLinks derives entity markup from the copy in place, so the machine-readable identification of the subject stays attached to the passage rather than living in a page-level template the extracted section leaves behind. Where the CMS owns markup output, we take the entity reference and leave publication there.
  • Screaming FrogThis page's own framing, that LLM SEO addresses information a model may encounter while browsing or retrieving sources at inference time, starts with a Screaming Frog crawl under the relevant bot's user-agent to establish page access, before content clarity or entity consistency is even assessed.
  • DiffbotBecause this page's own point is that a retrieval-time fragment may carry the whole answer, Diffbot's fact-and-entity extraction is used to preview what a retrieval pipeline is likely to pull from a given page, showing whether the extracted fragment actually preserves the claim's qualifications or loses them outside full-page context.

Crawl and rendering for AI agents

  • Prerender.ioThis page's boundary is that current web content may enter a browsing or retrieval flow, which only holds if the content is in the response. Prerender.io serves a rendered snapshot to declared bots, which closes that gap on a client-rendered application without a rebuild. It is a mitigation rather than a fix: we still record that the underlying delivery depends on JavaScript, because a snapshot service is one more thing that can fail.
  • Cloudflare AI Crawl ControlThe trace this page describes starts at crawler access, and the honest answer to whether a named bot reached the site comes from the edge rather than from a robots.txt reading. Cloudflare's AI Crawl Control reports allowed and blocked request counts per AI crawler, which distinguishes a deliberate policy from an accidental WAF rule. It applies to sites served through Cloudflare; elsewhere the same evidence comes from server logs.

Follow the retrieval path

Mark the boundary between your inputs and the model

Bring a small set of real questions and the platforms where they matter. We will follow the information from crawler access to retrieval and source support, then show exactly where your influence stops.
Inspect the weak points

The limits around the model layer

What LLM SEO can reasonably influence

What is LLM SEO?

LLM SEO improves web content and source signals that large language model systems may encounter during browsing, retrieval, and source-backed answer generation.

Can LLM SEO change a model's training data?

You cannot directly place your site in a third-party model's training set or edit the model's weights. Depending on the platform, current web content may still enter a browsing or retrieval flow.

Why does passage structure matter for LLMs?

Retrieval systems often select a limited section of a page, so that section has to carry its own subject, claim, evidence, and necessary qualification.

How do you measure LLM SEO?

We use a versioned set of prompts, markets, and platforms. Each run records mentions, answer claims, and cited sources. The report keeps the sample limits visible and describes only what appeared in the observable sample. It does not claim to explain everything happening inside the model.