AI Visibility Audit — AEO / GEO Consultant

Run a full AI-search visibility audit for one brand across the queries that matter to it. Where the tracker monitors single queries, the audit answers the consultant question: how visible am I in AI answers overall, who is eating my share of voice, and exactly what do I fix? — delivered as structured rows you can pipe, not a prose report.

Output (structured rows, two shapes)

row_type: "query" — one per audited query:

Field	Meaning
ai_overview_present / brand_in_ai_overview	Did the AI answer this query, and are you in it
citation_gap	true = the AI answers without you — your opportunity
outreach_targets	The domains the AI cited on a gap query — go earn a mention there
brand_organic_rank	Classic organic position (or null)
visibility_score	0–100 per query
recommended_action	The concrete next move for this query

row_type: "summary" — one roll-up:

Field	Meaning
overall_visibility_score	Average across all queries
ai_overview_coverage_pct	Share of queries that surfaced an AI Overview
brand_ai_share_of_voice_pct	Of the AI Overviews, how many include you
citation_gaps	How many queries answer without you
top_competitor_domains	Who the AI cites most across your queries (share of voice)
outreach_targets	The deduped, ranked outreach pool
recommended_focus	The single highest-leverage focus

How to use it

Feed outreach_targets straight into a contact-details scraper to find the site owners and request a mention — that is how AEO link-building actually works (~85% of AI brand mentions come from external domains).

Access & pricing

A protected target — runs through the Apify GOOGLE_SERP proxy, proxy from the first byte. Pay per row (PPE). Re-run weekly/monthly: 40–60% of AI citations change month-to-month, so a one-time audit goes stale. Results are localized — set countryCode / languageCode per market.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.