Start with a reproducible baseline

GEO Strategy & AI Search Audit

Your team asks the same brand question across selected AI engines and gets different descriptions or sources. We preserve those answers, reproduce the gaps, and test the evidence around them. The resulting roadmap names the owner, measure, and stop rule for each decision.
GEO Strategy & AI Search Audit service artwork

Some of the 500+ brands we've worked with

See all references
  • Sporx
  • ETS Tur
  • Tosla
  • Vitra
  • Bluemint
  • Axa Hayat Emeklilik
  • Amazon
  • BMW
  • Shell
  • Hyundai
  • PepsiCo
  • Red Bull
  • Decathlon
  • MediaMarkt
  • Bayer
  • Sanofi
  • EY
  • KPMG
  • GE
  • 3M
  • Domino’s
  • Lexus
  • Trendyol
  • Hepsiburada

Diagnose before implementation

Work out what is happening before choosing a fix

The audit gives you a versioned record of current visibility and the constraints behind it. A finding moves into delivery only when the evidence holds up, with its dependencies and decision rules still attached.
AI Search Visibility Audit & Baseline service artwork

AI Search VisibilityAudit & Baseline

Record what selected AI platforms say, cite, and leave out about your brand. Then examine the technical and source evidence behind each pattern.
GEO Roadmap & Measurement Design service artwork

GEO Roadmap& Measurement Design

Decide whether a finding needs a fix, a test, monitoring, or no action yet. Every approved initiative receives an owner and a measurable rule.

What the audit can answer

A visibility score only records what happened

We start with what the platforms visibly return. Your teams can then check crawl access, rendered content, entity facts, claim support, cited sources, and first-party outcomes. The surrounding evidence tells us whether the observation supports a diagnosis.

After the diagnosis

The evidence travels with the finding

Once reproduced, technical, content, entity, authority, and measurement findings move to the relevant GEO capability. Strategy keeps the priority, owner, acceptance rule, and original evidence connected so the delivery team does not have to repeat the investigation.

Scope and ownership

The evidence travels with the finding, so delivery never repeats the investigation

The sample is agreed before collection, which keeps prompt selection tied to real decisions rather than flattering ones.

Your audiences, markets, journey stages, platforms, and run conditions define the panel before anything is collected, and raw responses stay stored with their citations while specialists inspect crawl access, rendered text, entity facts, page support, recurring sources, and any first-party outcomes available for comparison. Observations, proxies, hypotheses, and constraints are labelled separately, and unresolved platform behaviour stays an explicit unknown rather than a soft conclusion. Strategy keeps the priority, owner, acceptance rule, and original evidence connected; technical, content, entity, authority, and measurement findings move to the relevant GEO capability with that record attached.

Build the plan from evidence your team can replay

The baseline comes from a representative question set. Business and delivery owners review the choices after specialists have tested the evidence behind each answer.

  1. Agree the sample before collection

    Your audiences, markets, journey stages, platforms, and run conditions define the panel. Agreeing them first keeps prompt selection tied to actual decisions, including results that present the brand unfavorably.

  2. Save the answers with their sources

    Raw responses and citations stay together. Specialists inspect crawl access, rendered text, entity facts, page support, recurring sources, and any first-party outcomes available for comparison. The record remains versioned.

  3. Mark the limits of the evidence

    Observations, proxies, hypotheses, and constraints receive separate labels, while unresolved platform behavior remains an explicit unknown.

  4. Make the funding decision

    A validated finding may be fixed, tested, monitored, deferred, or rejected. Its dependencies, owner, measure, review window, and stop rule remain part of that decision.

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.

Odeabank

Organic Sessions 217%+, Growth in Top 3 Keywords 60%+

İstikbal

87% Increase in Organic Sessions!

Meteksan

103% Increase in Organic Visibility and 500% Increase in SEO Visibility

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 Aybüke Göktuna5 Critical Mistakes to Avoid in GEOArticleby Can MutioğluGenerative AI Optimization (GEO) for E-Commerce SitesArticleby 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

  • ProfoundThis page's own first step, agreeing the sample before collection, is what Profound's panel-based tracking exists to do: the same question set runs repeatedly across the platforms and markets in scope, and every answer is retained with its date, model, and mode, which is the raw material the roadmap's baseline gets built from.
  • Peec AIWhere the audit needs to separate a branded question's result from a non-branded category question, Peec AI's prompt-level tags are what keep that distinction visible instead of collapsing everything into one score, which matters directly to this page's own point that a single visibility number cannot explain a pattern.
  • Otterly.AIThis page treats evidence that cannot separate two explanations as an explicit unknown. Running the agreed panel through Otterly.AI as well as the primary tracker is how that rule gets applied to the tooling itself: when one platform reports a mention the other missed, the disagreement is recorded rather than resolved by preferring whichever vendor the account happens to run. A single tracker's silence is not the same as a brand being absent from an answer.
  • SemrushThe constraint map this page produces has to separate an AI-visibility problem from an ordinary search problem, and that needs the conventional picture beside the AI one. Semrush supplies the demand, ranking, and competitor baseline the audit reads against, so a brand missing from an answer about a topic it never ranked for gets diagnosed as coverage rather than as an AI-specific constraint.
  • AhrefsWhen the audit finds a competitor cited where the client is not, this page requires testing the observable explanations before calling it unexplained. Source strength is one of them. Ahrefs gives the referring-domain and page-level comparison that says whether the cited page is simply better established, which either accounts for the gap or removes authority from the list and points the diagnosis at access, entity, or content support instead.
  • OpenAIThis page insists the sample is versioned and reported by platform, and that unflattering questions stay in the panel. ChatGPT is one of those platforms, and running the panel against it directly is what produces the raw answer the audit retains. We log whether browsing was active on each run, because a grounded and an ungrounded answer to the same question are not the same observation and cannot share a series.
  • Google GeminiThe audit reports by platform rather than blending, so Gemini runs as its own series against the same versioned question set. Keeping it separate is what makes a platform-specific constraint visible: a brand that appears in Gemini and not in ChatGPT points the diagnosis somewhere different from a brand missing in both. Merging the two would hide exactly the difference the constraint map exists to find.

Entity and structured data

  • Screaming FrogOnce a material pattern is confirmed in the panel data, this page's diagnosis step inspects technical access alongside entity facts, content support, and source exposure; Screaming Frog is the crawl that answers the technical-access half of that question before the roadmap assigns the finding to a specific method.
  • Google Search ConsoleBefore concluding that an AI engine is choosing not to cite a page, the audit checks Search Console to confirm the page is indexed and served at all, since an indexing problem and a selection problem call for different roadmap items entirely.

Content evidence and sourcing

  • NotionThe audit's final handoff, a roadmap that names the owner, measure, and stop rule for each decision, gets written and shared as a Notion document, since a roadmap only funds work if every team it assigns work to can open and reference the same record.

Measurement and reporting

  • Google AnalyticsThis page's deliverable is a decision, including the option to defer the work. That decision needs the AI-visibility sample reconciled against what the business already sees, and GA4 is where the referral and conversion side of that reconciliation lives. A visibility gap on a question that drives no measurable behavior is a legitimate reason to defer, and this is the record that lets the audit say so.

Establish the baseline

Find out whether GEO work is justified

The first sample uses the markets, questions, and platforms your team cares about. You leave with evidence that supports a decision, including the option to defer the work.
Plan the first sample

Before the first sample

Practical questions about the GEO audit

Should we start with an audit or go straight to GEO implementation?

An audit is useful while the cause of weak or inaccurate AI visibility is unclear. If your team has already reproduced a constraint and the evidence is usable, the specialist capability can take it on directly.

Which AI platforms, markets, and questions do you include?

We agree the panel before collection around your audiences, markets, journey stages, business priorities, and available access. The sample is versioned and reported by platform. Questions that present the brand unfavorably stay in the panel. That matters because prompt selection can otherwise skew the baseline.

Can you prove why a model mentioned, cited, or omitted our brand?

We can test observable explanations involving access, rendering, entity consistency, content support, and source fit, while hidden model logic remains inaccessible and evidence that cannot separate two explanations is recorded as an explicit unknown.

What happens after the strategy and audit work?

You receive the baseline, constraint map, validation backlog, and sequenced roadmap, with every recommendation linked to its observation or hypothesis. Approved work moves to the relevant technical, content, entity, authority, or measurement team together with its evidence, owner, acceptance rule, and review point.