WhatIsGeo documentation

R&D Unit

With the rise of generative AI-based search engines, content optimization has fundamentally changed. WhatIsGeo documentation explores this new reality through scientific methodology and validated experiments.

The context of GEO

Semantic transition redefines technical visibility. In Large Language Model (LLM) ecosystems, relevance is determined by an information node’s ability to be identified as the source of highest trust and semantic density for a given context.

Our research lines aim to map the citation and recommendation criteria used by autonomous agents and response engines.

Our scientific approach

Real-world experiments

Active

Stress testing in controlled environments to isolate citation variables.

Validated data

Verified

Results based on real search logs and LLM behavior.

Transparency

Open Source

Open methodologies for technical peer review and replication.

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All experiments utilize anonymized datasets to protect the integrity of participating brands.

Participate in the investigation

We are constantly validating new hypotheses regarding generative AI behavior. If your organization wishes to apply these methodologies and contribute data to our research lines, we invite you to become an Experimental Case.

Your brand could be the next experiment

Access our application protocol and help define the standards of GEO.

Enroll organization for validation