ANSWERS

How do AI models decide which brands to recommend?

Claude, ChatGPT, and Gemini draw on trained knowledge and retrieved sources when they name a brand. What gets recommended depends on the user's intent and context, the model, and the sources retrieved for that question. No provider publishes a universal formula. ye5's GEO Brand audit measures the answers your brand gets and the sources those answers cite.

What feeds a recommendation: trained knowledge and live retrieval

A recommendation draws on two separate inputs. Both are used, and how they are used depends on the question asked and the model.

—Trained knowledge — what a model learned about your category during training, fixed until the next release.
—Live retrieval — current pages a model fetches at query time, including facts that postdate training.
—Consistency — a claim that reads the same way across sources is easier for a model to interpret.
—Credible evidence — independent sources that support the same claim back it up.

GEO work targets both inputs — what a model already holds about your category, and what it finds when it checks live. How fast the training and retrieval clocks move is covered in (see: How long does GEO take to show results?).

Consistency and credible evidence help interpretation, not a known formula

Consistent claims and credible evidence help a model interpret your brand. No provider publishes a common weighting formula for those signals. Outcomes vary by the user's intent and context, the model, and the sources retrieved for that question.

Those quality signals run alongside the ones search engines have long used: (see: Does E-E-A-T matter for AI answers?). They apply across the sources a model reads, not only your own site.

What form a recommendation takes depends on the system

AI systems can search ranked indexes. They can return a single synthesized answer, a ranked list, or several brands together, depending on the system and the query — Do AI assistants recommend brands differently than Google ranks them? breaks down that difference in full.

What this means for your brand's inputs

Your brand doesn't control the trained-knowledge side, and it doesn't control most of the pages retrieval draws from either. The lever is the input pool: consistent, credibly evidenced claims published across the surfaces a model checks before it answers a customer's question. ye5's GEO Brand audit measures the answers your brand receives across Claude, ChatGPT, and Gemini and the sources those answers cite. It flags claims that contradict each other.

FAQ

Does a model's trained memory ever override what it retrieves live?

Both trained knowledge and retrieved sources contribute to an answer. Which one matters more depends on the question, the model, and what retrieval returns for that request. No provider publishes a formula for the balance.

Can a brand influence which sources a model weights most?

A brand can publish clear, consistent, credibly evidenced claims on the surfaces a model checks. From there, the practical step is measuring the answers your brand gets and the sources those answers cite.