Avenq.aiAvenq.ai Visibility Scorecard
Score what AI systems can actually read about this business — out of 120.
Avenq.ai runs the same six-dimension rubric used in the paid audit against the site itself: entity clarity, structured data, crawler access, answer structure, corroboration, and freshness. Twenty points each, scored against fixed criteria, so a second scorecard is always comparable to the first.
What it measures
Six dimensions, scored the same way every time.
Entity clarity
Can a machine tell what this business is, consistently, everywhere it's mentioned?
Structured data
Is the schema valid, connected, and does it match what the page actually says?
Crawler access
Can AI crawlers actually fetch the pages, or is content locked behind JavaScript?
Answer structure
Does content answer a real question in the opening lines, or is it buried in marketing prose?
Corroboration
Does anything off-site back up the claims made on the page?
Freshness
Is the content recent enough to fall inside an AI engine's typical citation window?
What the total means
The same four bands are used for every client, so a score means the same thing across audits.
A 72 with a zero in crawler access is a different engagement from a 72 that's evenly weak — the six components are always shown alongside the total, never averaged away.
Total
Reading
What's next
0–39
Machine-invisible
Usually one or two blocking issues dominate — fixing them first often moves the needle fast.
40–69
Partially readable
Fragmented signals across the site — full audit plus implementation.
70–94
Readable
Gains now come from corroboration, freshness, and answer structure.
95–120
Strong
Remaining upside is off-site and editorial, not on-page.
Method controls
Every score has to be traceable back to a fetched page, not a guess.
Fetched, not assumed
Every score is backed by a fetched page, a live crawl, or cited evidence — never a guess from a config file or a client's word.
Same rubric, every audit
The scoring bands don't move between engagements, so a second scorecard is always comparable to the first.
Schema checked against the page
Structured data that contradicts what a page visibly says scores worse than no structured data at all.
Ordered by impact, not dimension
The roadmap coming out of a scorecard is sorted by impact ÷ effort, not by which dimension happens to be lowest.
Scoring
Six dimensions, twenty points each — nothing rounded up to make a deliverable feel better.
Site Score
6 dimensions × 20 points = 120
Reported with all six components shown separately, so the client sees exactly which part of the site is holding the total down.
What the client gets
Measurement first. Roadmap second. Implementation only after approval.
AI-readability snapshot
All six scores, the specific URL and evidence behind each one, and the issues actually holding the total down.
Prioritized fix list
Every finding labeled observed or inferred, ordered by impact ÷ effort, specific enough to execute without a follow-up call.
Comparable re-score
The same fixed rubric run again after changes are live, so improvement is measured, not asserted.
Start measured
Begin with the baseline before buying deeper work.
Avenq.ai will not invent certainty. The first step is to see what AI systems can actually read on the site today.
Start audit request →