Veridian is not a real company and this scan never ran. Every number, crop, and finding below is illustrative — it shows the structure of a real report, not a real result. Your own report uses the same contract with your page’s measurements in it.
AUDIENCEOperations leads, 20–200 person teamsREQUEST
TRAFFIC SOURCEGoogle AdsREQUEST
LANGUAGEenINFERRED
CATEGORYWork managementINFERRED
SCAN ID7c1f9a02-4e6b-4c31-9d18-2f5ab0c7e114
CAPTURE VERSIONplaywright-capture-v2
RULE SETdeterministic-rules-v3
RUBRICpublic-audit-rubric-v1
SCORINGpublic-audit-score-v3
Scores
Four scores, kept separate on purpose. A readiness number is an audit measurement, not a prediction of what your conversion rate will do.
64.4/ 100OVERALL READINESS
70% conversion + 15% technical + 15% SEO
61.5Conversion Readinessweight 70%
68.0Technical Healthweight 15%
74.0SEO Healthweight 15%
82.0Evidence Confidencenot weighted
CONVERSION READINESS, BY DIMENSION
Message clarity and relevance25%54
Offer and persuasion20%61
CTA and journey friction20%66
Trust and objection handling15%72
Visual hierarchy and readability10%70
Mobile and interaction resilience10%48
What already works
Named first, so a correction never quietly breaks something the page was already doing well.
The primary action uses one consistent label in all four placements. Nothing competes for the same click.
Heading order runs H1 → H2 → H3 with no skipped levels, and focus order follows the visual order.
The product screenshot shows the actual interface rather than an abstract illustration, which supports the headline claim.
No layout shift above the fold on either viewport. Cumulative Layout Shift measured at 0.02.
Top three priorities
Ranked by severity × goal relevance × visitor exposure × confidence ÷ effort. Nothing here is ordered by how easy it was to spot.
All findings
Every normalized finding, deduplicated, with its evidence, its scoring inputs, the correction proposed, and how you would test it. Severity and confidence never merge into one number.
Performance
Lab diagnostics from this scan. Field data from real users is collected separately and never merged into these numbers.
2.9 sLargest Contentful Paint
0.02Cumulative Layout Shift
140 msTotal Blocking Time
1.4 sFirst Contentful Paint
71Lighthouse performance
3.4 MBTransferred, mobile
Lighthouse 13.4.1, mobile emulation, single run. A single lab run is a diagnostic, not a distribution — treat these as pointers to the finding below, not as your users’ experience.
Correction proposals
Each correction is a proposal. Copy changes are exact replacements; style changes are restricted to an allowlisted patch contract. Nothing is written to the live site at any point.
Preview rescan comparison
The three corrections above were applied in an isolated sandbox and the preview was rescanned as an immutable child scan. This is what changed.
That delta is audit evidence that specific findings stopped reproducing. It is not a predicted conversion uplift, and it does not tell you which variant would win a test. The validation plan on each finding tells you how to find that out.
Limitations
Stated in every report, because what a scan could not see changes how you should read what it did.
Only the public page was read. Nothing behind the sign-up form was captured, and no form was submitted.
Two findings are model-inferred hypotheses, not measurements. They carry lower confidence and are labelled as hypotheses throughout.
Product category and language were inferred, not supplied. Inferred context reduces the confidence of every finding that depends on it.
No analytics property was connected, so no finding carries an analytics-observed label. Connecting Search Console, GA4, or PostHog adds that third evidence class.
Performance figures are one lab run on emulated mobile hardware. They do not describe your real users’ distribution.
This report does not predict conversion rate, guarantee uplift, or tell you which variant will win. It tells you what is wrong and how to test the fix.
How to use this report with AI
Give an AI coding assistant one finding at a time together with the relevant source files. Keep the evidence and validation plan attached so implementation does not turn a measured issue into an unsupported redesign.
Safe remediation workflow
Choose one prioritized finding and locate the source responsible for the cited selector or asset.
Give the AI the finding, evidence, correction, constraints, and validation plan.
Ask for the smallest change that addresses only that finding.
Review the diff, run the stated validation, and rescan before accepting the change.
Do not ask the AI to invent analytics, business outcomes, or evidence the scan did not capture.
Prompt template
You are fixing one LandingQA finding.
Finding: [paste claim]
Evidence: [paste cited measurements/selectors]
Correction: [paste proposed correction]
Validation plan: [paste validation plan]
Relevant source files: [attach files]
Constraints: preserve unrelated behavior and existing design.
Implement the smallest safe change. Explain the diff, run the validation plan, and call out anything the supplied evidence cannot prove.
That was a fictional page. Yours can take a few minutes.
Same contract, same nine pillars, same evidence, validation plans, and AI remediation guide — saved in your account and downloadable as Markdown.