AI doesn't rank pages. It repeats facts it trusts.
A generative AI model gives an answer, not an ad: before it names a supplier, it needs facts it can verify, cross-check against other sources and quote in structured form. To be recommended, you have to become that kind of source, and that is the fundamental difference between GEO and traditional SEO.
In search, the buyer does the comparing. In an AI answer, the model has already compared for them and kept a few names. Your job shifts from winning a click to making the cut.
Classic SEO: the buyer still does the searching
Buyer searches
Ten blue links
Opens and compares
Lands on your site
You compete for a rank on the list
GEO: AI gives the answer directly, with no second chance
Buyer asks AI
AI writes one answer naming 3–5 suppliers
Sends an inquiry
You compete for a place on the shortlist
The key difference: rank #8 in search and someone may still scroll to you. Leave AI's answer and the buyer never learns you exist.
Dimension
Traditional SEO
GEO (Generative Engine Optimization)
Goal
Rank high in a list of results
Be named and cited inside the generated answer
Unit of competition
Keywords and pages
Facts, parameters and entity relationships
Content format
Marketing copy written for people
Structured technical source content written for models
Before AI names a supplier, three things have to be true: it can read your facts, it can confirm them somewhere else, and it can connect them to the buyer's exact question. Each capability below handles one of those.
1 · Readable
Semantic restructuring and Schema markup
We add Organization, Product, TechArticle, FAQPage and other structured data markup to your website, and release the key selection parameters in your TDS and COA documents from PDFs into machine-readable fields. Every category has different critical fields, so we first agree with your technical lead on exactly which ones drive a buyer's decision.
Product entities and parameter fields modeled
llms.txt and AI crawler access checked
Technical documents converted to indexable HTML
2 · Verifiable
Authoritative content seeding and external cross-verification
Models are cautious about what a single source says about itself, but they trust a fact that several independent sources state consistently. We systematically publish expert-level content in industry technical media, professional communities, LinkedIn and document platforms.
Scheduled long-form technical and application articles
Entries on industry platforms and in databases
White papers and selection guides
3 · Relevant
Competitor-model mapping and the "alternative to" strategy
We build a spec-by-spec mapping between your grades and the international big-brand models they compete with. When a buyer asks AI for "an alternative to model X," the model then has evidence to put your grade forward as a candidate.
Competitor-model parameter mapping library
Content for each substitution scenario
Application × system × recommended grade selection table
Delivery process
Four phases, each with a deliverable you can sign off
1Week 1
AI visibility audit
4 AI models tested
We map the questions your buyers actually ask, then test how often you and your competitors are recommended in the four major AI models (ChatGPT, Perplexity, Claude and Gemini).
Deliverable: AI Visibility Audit Report with real screenshots and a gap analysis.
2Weeks 2–5
Semantic asset build
Schema + specs
Schema markup on your website, structured product parameters, a competitor-model mapping library and application selection tables.
Deliverable: Checklist of structured data now live, plus the model mapping library.
3Ongoing from week 4
Authoritative content seeding
Cross-verified external content
Long-form technical articles, white papers and industry platform content, published on a weekly schedule so independent sources confirm each other.
Deliverable: Publication log and record of external indexing.
4Monthly
Monitoring and iteration
Monthly mention-rate report
We re-test your AI mention rate and recommendation position and adjust the content strategy based on the data.
Deliverable: Monthly AI mention rate report and next month's schedule.
Re-tested monthly; the data sends us back to phases 2 and 3.
Measurement
We report numbers, not “it feels better”
You can reproduce every one of these yourself in the models.
Mention rate
The share of target-category questions in which your brand is named.
Recommendation position
Where you appear in the shortlist AI gives.
Cited sources
Which pages AI cites: your own website or third-party source content.
Parameter accuracy
Whether AI repeats your product specs correctly.
Not sure it's worth it? Start with the audit
We test your brand and your main competitors in ChatGPT, Perplexity, Claude and Gemini, using the questions your buyers actually ask. Within 3 business days you get an AI Visibility Audit Report with screenshots. Decide after you've read it.
