Sample deliverable · Illustrative

What a Scorecard looks like.

The AI Visibility Scorecard is the diagnostic behind the 316-institution benchmark. Below is an illustrative sample, not a real institution. Your Scorecard is built on your own site, your own citations, and your own peer set.

Sample Community Credit Union
Approx. $2B assets · Midwest · benchmarked against 41 asset-band peers
Illustrative sample
58/ 100 overall AI Visibility 41st percentile
vs peer band
GEO / AEO44 / 100
28th percentile. How well answer engines cite the institution on buyer-intent questions.
SEO71 / 100
62nd percentile. Traditional search foundation the AI layer still draws on.
Technical66 / 100
55th percentile. Schema, structured data, crawlability, and page structure.
ADA Compliance52 / 100
39th percentile. Accessibility gaps that also read as quality signals to engines.
External Reputation Signals49 / 100
34th percentile. Wikipedia, third-party citations, reviews, and the sources AI trusts.
Findings (excerpt)

What the score actually means.

Answer engines are citing an outdated source

ChatGPT and Perplexity describe the field of membership using a 2019 press release rather than the current charter, so prospective members get a narrower answer than the truth. The institution's own site is not the source the engines reach for.

No independent reference layer

With no Wikipedia presence and thin third-party citations, AI answers default to aggregator and directory sites. The institution has little say in how it is described, because it has not built the reference layer engines weight most.

Remediation (excerpt)

The first thirty days.

Every Scorecard ends with a sequenced plan. This is the opening slice of a sample one.

  • Publish an FAQ schema block answering the eight most-asked membership and product questions in the institution's own words, so engines can quote the source directly.
  • Draft and submit a Wikipedia page with independent citations to establish the reference layer that is currently missing.
  • Correct the outdated press release the answer engines are anchoring on, and add current charter language where they look for it.
  • Close the two highest-severity ADA gaps, which double as quality signals to the engines.
Your turn

See your own numbers.

This one is illustrative. Yours would be built on your institution, your citations, and your real peer set, then sequenced into a plan you can act on.

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