Sitewide AI visibility audit
A sitewide AI visibility audit is RankEcho's brand-level visibility intelligence workflow. It checks whether AI engines understand, cite, mention, ignore, or replace your brand across a stable set of buyer-intent prompts, then turns the evidence into a prioritized map of competitor losses, source gaps, crawler access issues, and next fixes.
What does a sitewide audit answer?
A sitewide audit answers the executive question: when buyers ask AI systems about the category, does the brand show up, and if not, who replaces it?
The audit looks across the brand rather than one URL. It helps teams understand market-level visibility, competitor replacement, source dependence, prompt classes, and whether public brand signals are strong enough for AI systems to cite.
- Which buyer prompts mention or cite the brand.
- Which prompts recommend competitors instead.
- Which engines show the biggest visibility gap.
- Which source types AI trusts for the category.
- Which fixes should be prioritized first.
How is this different from a page-level audit?
A sitewide audit diagnoses the brand's overall AI-search presence. A page-level audit diagnoses one URL and turns that page into an optimization workflow.
Use sitewide when the team needs a baseline, board-level story, client intake report, or strategic visibility map. Use page-level when the team already knows the target page, such as a product, pricing, comparison, solution, or guide page.
What RankEcho measures sitewide
RankEcho measures prompt outcomes across AI engines and classifies what happened: cited, mentioned, absent, replaced by a competitor, or skipped because an engine was unavailable.
The scorecard is intentionally evidence-based. It reports citation rate, share of voice, prompt coverage, competitor replacement, engine coverage, source mix, and page-level opportunities that should become the next optimization queue.
- Citation rate and mention rate.
- Share of voice against named competitors.
- Prompts lost to competitors.
- Owned versus third-party source dependence.
- Engine coverage and skipped-engine transparency.
- Page-level workstream candidates.
What the buyer sees
A strong sitewide result page should not feel like a vanity score. It should show audit depth, prompt and engine coverage, a prompt-by-engine matrix, source evidence, competitor losses, and recommended fix sequence.
For agencies, the same output becomes a client intake artifact: what AI currently says, where the client is missing, why competitors win, and which fix path should start first.
How the sitewide audit feeds the Fix Engine
The sitewide audit creates the backlog. It identifies the highest-value prompts where the brand is absent or replaced. The Fix Engine then turns each selected gap into a shippable package.
Some gaps become page-level tasks. Others become source-coverage tasks, entity-clarity work, crawler fixes, or comparison content. The important move is that the sitewide audit decides what to fix next instead of leaving the team with a generic score.
When should teams run it?
Run a sitewide audit before starting AI search visibility optimization, before selling an agency engagement, after a brand or product positioning change, and after major content/source work has shipped.
For active teams, the sitewide audit becomes the quarterly or monthly strategic layer, while page-level audits handle the specific optimization work between baseline checks.
Frequently asked questions
The initial RankEcho audit is free and gives teams a visibility baseline. Paid plans unlock deeper fix generation, proof-loop tracking, exports, and higher prompt volume.
No. Sitewide means brand-level prompt intelligence across the domain and category. RankEcho uses the domain, source evidence, prompt outcomes, and page opportunities to identify where the brand is visible or missing.
Start with the highest-intent competitor loss or access issue. Then generate a fix package and use page-level audit or proof-loop tracking to verify movement.
Yes. The sitewide audit is designed to become a client-ready baseline: current visibility, competitor risk, source gaps, and the first recommended workstream.