What each screen does, what each number measures, and what to do when something looks wrong. For the ideas behind AI search, see the Concepts section below.
Set up a project and read your first results.
How sign-in works, what a project is, and what you need before you start.
The four setup steps, and what we read from your website along the way.
What runs, when it runs, and how long before you see data.
A tour of the dashboard, and which number to look at first.
Prompts, citations, gaps, and competitors.
What a prompt is, how it differs from a keyword, and how the ones you track get run.
Where suggested prompts come from, how auto refill behaves, and what a Search Console opportunity is.
What each engine actually searched for behind your prompt, and the eight categories those searches fall into.
What counts as a citation, how one is attributed to you, and what the screen shows.
How a gap between you and a cited competitor is detected, ranked, and actioned.
How competitors are discovered, what tracking one changes, and what the comparison shows.
Page fixes, articles, and domains.
Page-level recommendations checked against the sources AI engines actually cite, and what to do with one.
How a topic becomes a brief, a draft with its claims checked against the sources it names, and a published page tied back to the gap it closes.
What counts as a domain here, what an owned page is, and what each daily snapshot records.
AI referral traffic and search performance.
Sessions that arrived from an AI engine, split by engine, with the pages they landed on and what they did next.
Google Search Console data for your site: what you rank for, which positions moved, and where a small change would pay.
Google Analytics sessions grouped by where they came from, with AI counted first, and the pages where attention is not converting.
Weekly briefings, audits, alerts, and your inbox.
A dated report for each week, built from your own numbers with no model in the loop, readable on screen, by email, and on paper.
A weekly written briefing that reads your search and traffic numbers alongside outside research, and says what to do next.
A weekly audit of your live llms.txt against the spec, with a compliant file written out for you to publish.
A daily watch on your visibility that raises an alert when something moves enough to be worth reading, and shows why it fired.
Everything the product tells you about, where it is delivered, and how it differs from an alert.
Plans, team, integrations, and exports.
What is measured against your plan, what is simply on or off, and what happens the moment you cross either line.
Every field on the project record, who is actually allowed to change it, and what pausing and archiving really do.
The three roles a project membership can hold, what each is checked against, and what adding someone actually does.
Connecting a Google account and mapping a property are two separate steps with two separate scopes, and why that distinction matters.
Every downloadable dataset, what one row means in each, the row cap that refuses rather than truncates, and what the project activity log can actually show.
What a tag attaches to and where you can filter by one, and precisely what the keywords screen does today.
The ideas behind AI search, and the terms this product uses.
Generative engine optimization: getting cited by the systems that answer questions instead of listing links.
Answer engine optimization, how it overlaps with GEO, and why the distinction is mostly about scope.
What carries over from search engine optimization, what does not, and which habits actively mislead.
Why ranking first for a keyword does not make an engine cite you, and what does.
Mention, citation, retrieval and prompt: the four distinctions the rest of the product rests on.
What every number means, and how to fix problems.
See how AI search engines talk about your brand.
Free to start. No credit card required.