Method
The AI search optimization methodology behind every SIMLL program
One job, worked four ways: topical authority mapping, tested on-page execution, live SERP validation, and model-side training from the labs. Here is why that beats template SEO, in terms a business owner cares about.
The methodology
There is one job, not three products
Our founder has been a member of Koray Tuğberk Gübür's Holistic SEO Community since 2022, is a graduate of the Topical Authority Course, holds the Google AI Professional Certificate, and is a member of Kyle Roof's IMG. The first thing that training gives you is the discipline to stop selling AI search as a separate product. Google's own position, published in May 2026, is that optimizing for generative AI search is still SEO, and Gübür has said the same. There is no GEO department here and no AEO package, because there is no separate mechanism to sell you. There is one job: make a business the obvious thing to name when someone asks a question it should own. What that takes is complete coverage of a subject rather than a scatter of posts, a site an engine can read cheaply and segment correctly, and pages that answer the question they exist for without being hunted for. Those are the factors in the framework we work to, and the honest note is that one of them, the accumulated history of a domain, cannot be bought at any price. We build the ones that can be.
Discipline one
Topical authority: cover the territory, earn the trust
Search engines and AI engines trust sources that own a topic, not sites that visit it occasionally. Following Koray Tugberk Gubur's topical authority methodology, every SIMLL program starts with a full topical map of your market: every question a buyer asks, organized into hubs and supporting pages, each with one job and no overlap. That structure is why our pages get cited: an AI engine assembling an answer looks for the source that covers the whole territory.
Discipline two
On-page rigor: measured, not guessed
The second discipline is Kyle Roof's on-page SEO methodology: single-variable testing that proves which on-page factors move rankings, applied as a checklist to every page we publish. Word placement, heading structure, keyword positioning, and schema are executed to measurement, not taste. The result is a page that both Google's ranking systems and AI extraction can read without ambiguity.
Discipline three
DataForSEO validation: The Golden Rule
Nothing ships on assumption. Before any page is written, the topic is validated against live SERP data through DataForSEO: real volumes, real cost-per-click, real competition, and what the current results actually reward. We call it the golden rule: if the live SERP does not confirm the opportunity, the page does not get built. This is what separates a mapped program from a content calendar.
Discipline four
Model-side understanding: trained where the engines are built
Optimising for AI answers fails when it treats the engines as black boxes. Jose completed Anthropic's coursework on how Claude reasons and cites sources, and OpenAI's coursework on how ChatGPT assembles answers. That training shapes how we structure pages for extraction: answer-first openings, entities named explicitly, claims a model can lift and attribute. It is the difference between doing the work and guessing at it.
The workflow
From map to measurement, every time
Topical map
The full territory of your market, structured into hubs and supporting pages.
SERP validation
Every topic checked against live search data through DataForSEO before writing.
Entity matrix
The entities an authoritative answer must cover, mapped per page.
Brief
Structure, keywords, questions, and linking defined before drafting.
Draft
Written for a decision-maker, structured for AI extraction.
Publish
Schema, meta, slug, and internal links wired in on the way out the door.
Measure
Rank tracking plus AI citation monitoring: who cites you, who does not, and why.
This methodology, run for your business
Three tiers, everything done for you. See what each program includes.