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Resources / The answer layer

Learn to win the answer.

Field notes, playbooks and methodology on answer engine optimization, how AI engines decide who gets cited, and how to become that source.

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Field notes and playbooks

FUNDAMENTALS · 6 MIN

Why ChatGPT doesn't mention your brand

Six reasons ChatGPT leaves a brand out of its recommendations, how to test which one applies to you, and what to fix first. No guesswork, no promises.

PLAYBOOK · 5 MIN

How brands earn product recommendations in AI answers

RESEARCH · 7 MIN

AI visibility tools in 2026: the main categories and how they differ

TEARDOWN · 6 MIN

Profound, Peec AI and Otterly: how three AI visibility trackers differ

FUNDAMENTALS · 7 MIN

What is GEO? Generative Engine Optimization, explained from zero

PLAYBOOK · 8 MIN

How to choose an AI visibility tool: an 8-point evaluation checklist

PRODUCT · 7 MIN

What we get wrong: a methodology note on model-recalled citations

Some of the citations in AI answers were never retrieved from anywhere - the model recalled them from training. A note on how this limits our measurement, and everyone else's.

PRODUCT · 8 MIN

The 28-day loop: shipping a fix and proving it moved

PRODUCT · 7 MIN

Why a visibility score without attribution is a vanity metric

TEARDOWN · 8 MIN

Reading an AI answer like an analyst: position, framing, hedging

PLAYBOOK · 7 MIN

Should you allow GPTBot? The block-training / allow-search split

RESEARCH · 6 MIN

llms.txt: what it does, what it doesn't, and whether to bother

RESEARCH · 9 MIN

How answer engines choose sources - what the research actually shows

FUNDAMENTALS · 6 MIN

What “share of voice” means when there's only one answer

PLAYBOOK · 7 MIN

Entity consistency: the boring fix that moves AI visibility most

FUNDAMENTALS · 6 MIN

AEO vs GEO vs SEO - one job each, and where they overlap

PLAYBOOK · 7 MIN

How to get cited by Perplexity: what the retrieval layer actually rewards

Glossary

Answer-engine terms, defined

Share of voice, grounding, retrieval, llms.txt, what each one means and where people get it wrong.

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