Veridian is a controlled fictional page. The images below are real captures from that page; the company, findings, measurements, and scores are illustrative. Customer reports use this same production layout with evidence from the submitted website.
A practical view of the page’s message, offer, action path, trust, visual hierarchy, mobile experience, accessibility, SEO, and performance.
COMPLETED30 Aug 2026, 13:28 UTC
SCAN TIME2 min 37 sec
SCREENS CHECKEDDesktop and mobile
PAGE TYPESaaSDetected from page
MAIN GOALTrialProvided by you
AUDIENCEOperations leads, 20–200 person teamsProvided by you
VISITOR SOURCEGoogle AdsProvided by you
LANGUAGEEnglishDetected from page
PRODUCT CATEGORYWork managementDetected from page
What the scanner saw
The retained desktop and mobile captures make the report easier to verify. Select a preview to enlarge it, or open the complete page capture in a new tab.
Use these versioned indicators to understand this scan and compare a later rescan of the same page. The public PageSpeed result remains separate below.
64.4/ 100PROVISIONAL READINESS
70% conversion + 15% technical + 15% SEO
61.5Conversion Readinessweight 70%
68.0Technical Healthweight 15%
74.0SEO Healthweight 15%
82.0Evidence Traceabilitynot weighted · not correctness
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
Start here. The order considers visitor impact, relevance to the page goal, exposure, confidence, and implementation effort.
All findings
Each finding explains why it matters, what the scan saw, a concrete improvement direction, and how to check the fix.
Performance
The public score and the protected scanner diagnostic stay separate, so infrastructure overhead is not mistaken for visitor experience.
Mobile
Public PageSpeed scoreGoogle’s independent public-page lab test
68/100
Largest Contentful Paint2.9 s
Layout shift0.02
Blocking time140 ms
First content shown1.4 s
Lighthouse 13.4.1 · 30 Aug 2026, 13:26 UTC
Protected scanner diagnosticRuns through LandingQA’s safety network
57/100
Largest Contentful Paint3.5 s
Layout shift0.02
Blocking time190 ms
First content shown1.8 s
Lighthouse 13.4.1 · 30 Aug 2026, 13:28 UTC
Desktop
Public PageSpeed scoreGoogle’s independent public-page lab test
94/100
Largest Contentful Paint1.3 s
Layout shift0.01
Blocking time40 ms
First content shown0.8 s
Lighthouse 13.4.1 · 30 Aug 2026, 13:26 UTC
Protected scanner diagnosticRuns through LandingQA’s safety network
82/100
Largest Contentful Paint1.8 s
Layout shift0.01
Blocking time90 ms
First content shown1.0 s
Lighthouse 13.4.1 · 30 Aug 2026, 13:28 UTC
The public PageSpeed result is the customer-facing benchmark when it is available. The protected scanner diagnostic remains visible for technical context and can be lower because the scanner’s safety network adds overhead. Both are timestamped lab samples; real-user field data is shown separately when available.
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
This comparison comes from the retained parent and child example scans. It is audit evidence, not a predicted conversion uplift.
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.
Some findings are interpretive 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 controlled 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.
Report reference for support
Report ID
7c1f9a02-4e6b-4c31-9d18-2f5ab0c7e114
Capture
playwright-capture-v8
Checks
public-audit-rules-v8
Review rubric
public-audit-rubric-v1
Score formula
public-audit-score-v8
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.
Improvement framework
State the audience, offer, useful outcome, credible mechanism, and next action clearly.
Map each meaningful claim to real visible proof; weaken or remove unsupported claims.
Use one dominant action family and place truthful effort, price, or risk information near it.
Show authentic product behavior or output early.
Treat mobile as its own reading order and protect accessibility and performance.
Describe every recommendation as a testable hypothesis, never guaranteed uplift.
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.
You get the same contract, nine pillars, evidence, validation plans, and AI remediation guide. The report is saved in your account and downloadable as Markdown.