GEO Consulting
We make generative engines like ChatGPT, Gemini, Perplexity and Claude cite your brand. Not a ranking exercise, a measurable visibility programme aimed at becoming the answer itself.
Is this for you?
If one of these sounds familiar, you are in the right place.
- When you ask ChatGPT or Gemini about your category, competitors are recommended and your brand is missing.
- Organic traffic is flat while visits from AI platforms grow, and you have no way to manage that channel.
- Your content is strong, yet AI answers cite other sites as the source.
What is Generative Engine Optimization?
Generative Engine Optimization (GEO) is the discipline of getting your brand cited as a source inside the answer a generative engine gives. Classic SEO aims to rank on a results page; GEO aims to get inside the answer.
People no longer pick from ten blue links; they read a single synthesised answer. That answer draws on three to five sources, and if you are not one of them you effectively do not exist for that user.
So GEO is less about content volume and more about machine readability and trust: structured data the model can parse, clear entity signals, fact-based content and a consistent presence in third-party sources.
The output is tracked by measurement rather than guesswork: how often the brand appears across a fixed prompt set, in which position, and which of its pages get cited, reported on a regular cycle.
From content to a cited answer
Getting inside an answer is a three-step chain: access, understanding, selection.
Content and data layer
Fact-based content bots can reach, that reads without JS and carries schema markup.
LLM synthesis
The model compares sources and pulls in the one that is trustworthy, consistent and clear.
Citation and click
Your brand is cited in the answer; qualified, high-intent traffic follows.
A GEO approach for each AI platform
Every engine picks its sources differently, so a single tactic never works across all of them. We build the approach platform by platform.
ChatGPT · OpenAI
Search + memory- Source logic
- Fresh web content crawled by OAI-SearchBot plus strong entity signals; long answers come with a source list.
- Focus
- robots.txt permissions, llms.txt, crisp product and service definitions, third-party mentions.
Google AI Mode · Gemini
Search-based- Source logic
- Runs on classic search infrastructure; Google-Extended permission, schema depth and page experience decide the outcome.
- Focus
- Structured data, E-E-A-T signals, hreflang and technical health.
Perplexity
Citation-first- Source logic
- Ties every statement to a source; prefers clear, date-stamped pages carrying data.
- Focus
- Freshly updated content, tables and comparison blocks, PerplexityBot access.
Claude · Anthropic
Context-driven- Source logic
- Looks for consistency across long context; treats contradiction-free, fact-based content as reliable.
- Focus
- Claude-SearchBot access, on-page consistency, explicit scope and limit statements.
Be visible across every AI engine
How GEO differs from traditional SEO
The two are not alternatives; GEO is built on a solid SEO foundation and works toward a different goal.
| Criterion | Traditional SEO | GEO |
|---|---|---|
| Goal | Ranking on the results page | Being cited in the answer |
| Unit | Keyword | Question and intent (prompt) |
| Success metric | Position, clicks, organic sessions | Found rate, citation rate, share of model |
| Content format | Long form, keyword-led | Fact-based, citable blocks |
| Technical focus | Crawl, speed, indexation | Agent access, schema, entity clarity |
| Authority source | Backlink profile | Third-party mentions and consistency |
| Competitive field | Ten blue links | A single answer with 3 to 5 sources |
| Measurement rhythm | Daily rank tracking | Repeated prompt measurement |
Our GEO delivery framework
A four-phase programme that skips no steps. Each phase ships with its own output and measurement point.
AI visibility auditBaseline measurement
We fix 20 to 50 real purchase-intent prompts in the target market language, then measure how many of them mention the brand, in what position, and how that compares with competitors. Bot access, schema and entity signals are audited alongside.
0–30 daysData and infrastructure layerMachine readability
robots.txt and WAF permissions, JS-free rendering, Organization/Product/FAQ schema, llms.txt and hreflang are put in place. The goal is for the engine to read the content fully and correctly.
30–60 daysContent and citation optimisationCitability
Fact blocks, comparison and limit statements, FAQ/QA structures and freshness stamps are added. Entity authority is built through a consistent presence in third-party sources.
45–90 daysMeasurement and continuous improvementReporting loop
The prompt set is repeated monthly; found rate, citation rate and the AI-sourced session to conversion funnel are tracked on one dashboard. Content is prioritised by won and lost citations.
OngoingEntity
The brand identity recognised clearly and consistently by the model.
Schema
A machine-readable meaning layer built with structured data.
Passage
Self-contained blocks that can be lifted straight into an answer.
Citation
Verifiable authority in third-party sources.
What do you get with Webtures GEO?
The GEO approach of Webtures experts
Three principles: unmeasured work does not get done, data comes before content, claims are backed by evidence.
Measure first, intervene second
Every engagement starts with a fixed prompt set and is re-measured on the same protocol; improvement is a comparison, not an estimate.
The data layer comes before content
A page the engine cannot read will never enter an answer, however well written it is. Access and structured data always precede content.
Facts beat claims
Models look for facts, not adjectives. Content carrying measures, scope, limits and sources gets cited more often than content carrying claims.
Frequently asked, carefully answered.
Everything you want to know before starting your GEO project: from our approach to measurement, from reporting to team structure. Written by the people doing the work.
01 How does the GEO (Generative Engine Optimization) process make content AI-friendly?
The process works on three layers: the engine reaching the content (bot permissions, JS-free rendering), understanding it correctly (schema, entity clarity) and finding it worth citing (fact-based, self-contained blocks). With those three in place the content becomes readable and sourceable for the model.
02 What are the differences between traditional SEO and GEO, and why do they matter?
Traditional SEO targets ranked blue links; GEO targets earning a citation inside a generated answer. They share core principles but diverge in format, measurement and intent modelling. As AI-mediated search grows, a brand that does not appear inside the answer stays invisible to the user.
03 Do GEO and SEO need to run together?
Yes. GEO is built on a solid SEO foundation: accessibility, technical health and content quality matter to both. The difference is that the goal is being the source of the answer rather than a ranking, and that measurement is done at prompt level.
04 When will I see the first measurable results?
The effect of access and schema fixes usually becomes measurable within 30 to 60 days. Entity authority and citation share take 90 days and beyond; for smaller brands that period can be longer.
05 How is visibility measured, and do you guarantee it?
On a fixed prompt set we measure found rate, position inside the answer, citation rate and share of model against competitors. Because LLM answers vary from session to session, measurement is repeated. We do not guarantee a specific ranking or citation; our commitment is measurable progress and transparent reporting.
06 Is it suitable for brands outside e-commerce?
Yes. For B2B, service and corporate brands, service definitions, expertise content and entity authority take the place of a product feed. The framework stays the same, the weightings change.
07 Do you produce the content yourselves?
We can run both the content plan and the production; alternatively we prepare the plan and templates and leave execution to your in-house team. In both models the technical setup and measurement stay with us.
08 How does reporting work?
Each month the visibility dashboard, won and lost citations, the impact of technical changes and the next priorities are presented in a single document. The executive summary and the implementation list are delivered separately.