About Artur Ferreira

My name is Artur Ferreira. For more than two decades, I have worked
in SEO — building organic growth systems, scaling content strategies,
solving technical bottlenecks, and aligning search performance with
commercial outcomes.

For most of that time, search was a ranking problem. We optimised
for keyword targeting, link authority, technical crawl efficiency, and
SERP position.

But search is no longer just ranked. It is retrieved, interpreted,
compressed, and synthesised. Large language models, AI Overviews, and
generative search systems have introduced a structural shift. Visibility
is no longer defined solely by ranking position. It is defined by whether
your content is retrieved, understood, and cited inside machine-generated
answers.

That shift requires a new optimisation discipline. This site documents
my transition from traditional SEO strategy to Generative Engine
Optimisation (GEO) — publicly, rigorously, and experimentally.

Why I Built The GEO Lab

After 20 years in SEO, I noticed that most industry discussion about
AI search is reactive. It focuses on tool comparisons, prompt tactics,
and surface-level speculation. Very little focuses on structural
mechanics: how retrieval systems decide what to extract, how entity
clarity affects summarisation, how structured data reinforces machine
confidence, and how AI-driven search impacts commercial exposure.

The GEO Lab exists to explore those mechanics methodically. Not as
a hype cycle. As a systems problem.

What Is Generative Engine Optimisation

Generative Engine Optimisation (GEO) is the practice of designing
content to maximise its probability of being retrieved, extracted, and
synthesised within AI-driven search systems. Traditional SEO optimises
ranking probability. GEO optimises retrieval probability, extraction
clarity, and entity reinforcement.

What You Will Find Here

This is not a marketing blog. It is a documented evolution. At
The GEO Lab I publish controlled GEO experiments, content restructuring
case studies, structured data implementation tests, AI visibility
measurement frameworks, and early-stage tool development insights
built around the GEO Lab Console.

Each post is structured around a hypothesis, intervention,
observation, and business implication.

My Approach

Everything published here is guided by four principles. Systems
over tactics — AI search is architectural and surface tricks do not
scale. Evidence over hype — if it cannot be tested it is not strategy.
Commercial impact over vanity metrics — visibility matters only if it
supports revenue or strategic positioning. Evolution over nostalgia —
traditional SEO fundamentals still matter but must be adapted to
retrieval-based systems.

The GEO Stack

The GEO Stack is a five-layer framework for engineering generative
visibility developed at The GEO Lab. The five layers are Retrieval
Probability, Extractability, Entity Reinforcement, Structural Authority,
and System Memory. Each layer addresses a distinct aspect of how
generative search systems select, parse, and cite content.

Current Projects

The GEO Field Manual — a 90-page practitioner guide to Generative
Engine Optimisation, published February 2026. The GEO Lab Console —
a diagnostic tool measuring content extractability and retrieval
readiness at the section level, currently in development.