What an AuditMe Report Actually Contains
This page walks through the report end to end with static, code-rendered examples so you can see the shape of the output before you run anything: the score breakdown, the issue table, the priority queue and the re-check flow. All example values are labelled sample data.
How do you get from a URL to a verified fix?
Four steps, in order: enter a URL, get the audit, fix what the report ranks highest, then re-check the same URL to confirm the change landed.
- 1
Enter a URL
Paste any public URL. You get 1 free audit with the full check set before you need an account, and crawl limits scale with the plan.
- 2
Get the audit
The engine fetches the page, runs 159 registered checks across 16 dimensions, adds lab metrics and field data when those sources exist, and returns a weighted score with a fix queue.
- 3
Fix in priority order
Findings arrive sorted by impact against effort, each with the evidence that triggered it and the change to make. You decide what ships first.
- 4
Re-check
Run the audit again or use Verify Fix on a saved audit: the same URL is re-scanned and the report diffs fixed issues against the ones that still fail.
What is inside the report?
The report is built from a fixed 12-section architecture — the same list the PDF and the dashboard render, so nothing is improvised per customer.
- 01
Executive Intelligence
Overall score, grade, checks passed, top problems, top quick wins, potential recovery, confidence and the AI/GEO snapshot.
- 02
Dimension Health
Full health matrix across every audit dimension with per-dimension scores, grades and benchmark comparison.
- 03
Findings & Fix Queue
Every finding with evidence, why it matters, the concrete fix, impact and confidence — ordered as an actionable queue.
- 04
Priority Map
Impact × effort decision matrix: quick wins, big bets, fill-ins and long tail — each point linked to its evidence.
- 05
Search Result Preview
How the page appears in Google today versus the optimised preview, with title/description length checks and tag validation.
- 06
30-Day Action Plan
Week-by-week prioritised roadmap built from impact × effort, with the estimated score recovery per phase.
- 07
Revenue Impact Scenario
Illustrative revenue scenario modelled from the visibility gap — explicitly labelled as a scenario, not a forecast.
- 08
Crawl & Indexation Health
Robots.txt, sitemap, indexability, canonicals and redirects — with an explicit captured / not-captured status per source.
- 09
Performance Intelligence
Core Web Vitals and lab metrics with the data source shown for every value (lab, field, inferred or not captured).
- 10
AI/GEO + Agent Readiness
AI/GEO readiness, AI search readiness and agent readiness as separate measured layers, with per-signal breakdowns.
- 11
Detailed Findings
The complete check-by-check results across every dimension, with pass/fail status and evidence tiers.
- 12
Evidence Quality & Methodology
Data coverage per source (measured / inferred / not captured), evidence confidence, and the scoring methodology.
What does the score breakdown look like?
Each weighted group shows a sample score and a sample pass count. The structure is real — the values below are sample data for a fictional page.
| Weighted group | Checks (sample) | Sample score |
|---|---|---|
| Technical SEO | 41 of 52 | 78 |
| Content Quality | 26 of 37 | 71 |
| Meta Tags | 31 of 37 | 84 |
| Performance | 6 of 13 | 46 |
| Links & Authority | 10 of 15 | 66 |
| Schema & Structured Data | 2 of 9 | 22 |
| AI / GEO Readiness | 6 of 11 | 55 |
Your own report uses the same 7 groups; the 16 audited dimensions (including Meta Tags, Content Quality, Technical SEO, Links) roll up into them, and dimensions with no applicable checks are excluded instead of scored as zero.
What does the issue table look like?
One row per finding, with the real check id, the dimension it belongs to, its severity, the evidence tier that tells you whether it was measured, and the fix.
| Check | Dimension | Status | Severity | Evidence | Recommended fix |
|---|---|---|---|---|---|
| performance:lcp | Performance | Fail | Critical | Measured (lab, PageSpeed) — 4.2s | Serve the hero image as WebP, lazy-load below-the-fold images, preconnect to the font host. Target under 2.5s. |
| content:h1-present | Content Quality | Fail | High | Measured (HTML) — no H1 found | Add exactly one H1 that states the page topic, then keep the H2/H3 hierarchy beneath it. |
| schema:present | Schema | Fail | Medium | Measured (HTML) — no JSON-LD block | Add Organization and WebSite JSON-LD on the homepage; add Article or Product schema where the page type calls for it. |
| ai:llms-txt | AI Search Readiness | Fail | Medium | Measured (HTTP) — /llms.txt returned 404 | Publish /llms.txt with a short site description and links to your key pages and content policies. |
| meta:title-length | Meta Tags | Warning | Medium | Measured (HTML) — 74 characters | Trim the title to 50-60 characters and move the brand to the end. |
| meta:canonical | Meta Tags | Warning | Low | Inferred (heuristic) — canonical differs from final URL | Point the canonical at the final URL after the redirect so the signal stops disagreeing with the redirect chain. |
| performance:inp | Performance | Info | n/a | Not captured — no CrUX coverage for this origin | Nothing to fix here: the report states the source was not captured instead of estimating a value. |
Which fixes come first?
The priority map orders findings by impact against effort. Quick wins are high-impact and easy; big bets pay off but cost real work. The rows below are the sample set for this fictional page.
schema:present
Structured data is the fastest way to make a page machine-readable, and it is a single template change.
performance:lcp
The hero image dominates the lab LCP; compressing and lazy-loading it moves the largest single number.
ai:llms-txt
One file at the site root closes an AI crawler gap that no amount of on-page copy can offset.
meta:title-length
A 74-character title truncates in the search result; shortening it is a copy edit, not a rebuild.
The report also ships a 30-day action plan grouped week by week, plus an optional scenario section that is explicitly labelled a scenario rather than a forecast.
What this sample page deliberately does not show
- No customer names, logos, testimonials or before-and-after scores — the example domain is fictional.
- No "client X improved by N%" claims — progress is shown as a diff between two audits of your own site, never as a promised lift.
- No revenue guarantees — where a scenario appears in the real report it is labelled a scenario, not a forecast.
- No borrowed benchmarks — comparison references come from AuditMe's own internal dataset and say so on the page.
Frequently asked questions about the sample report
Is this a real customer report?
No. Every value on this page is sample data written for illustration, using example.com-style content. AuditMe does not publish customer results, testimonials or improvement claims here.
What does an AuditMe report contain?
The report has 12 sections: executive score and grade, dimension health, a findings and fix queue, a priority map, a search-result preview, a 30-day action plan, a clearly labelled scenario, crawl and indexation health, performance intelligence with a source per metric, AI/GEO and agent readiness, check-by-check detail, and evidence quality with the methodology.
How long does one audit take?
One audit runs on demand against a single URL: the page is fetched, checked and scored in the same request, with lab and field sources queried in parallel when configured.
Does the sample score mean anything for my site?
No. The sample breakdown is illustrative. Your own score comes from your own crawl, and dimensions with no applicable checks are excluded rather than scored as failures.
How do I prove that a fix worked?
Re-check. The verification flow re-scans the same URL and reports each issue as fixed or still failing, so you compare two audits instead of trusting one new number.