AuthorityMap Audit Checklist: What AI Engines Actually See When They Look at a Local Professional

There are now five places your buyers go when they ask "who should I work with." ChatGPT. Google AI Overviews. Perplexity. Gemini. Claude. The ones that cite you get the call. The ones that miss substitute you with a competitor.

Most local professionals have the proof to be cited. Reviews. Credentials. Years of transactions. Real referral relationships. The proof is just scattered across half a dozen platforms, partially stale, and structurally invisible to the engines that now mediate buyer trust.

This is the checklist we run during an AuthorityMap Audit. The same evidence-first methodology that produced our pilot report on a Spokane mortgage loan officer with a 20% mean engine hit rate at baseline. Identity was intact. Retrieval was the problem.

Run the checklist yourself. Or let CredibilityOS run it. Either way, this is what the work actually looks like. New to the idea? Start with what AI visibility is and why local professionals keep losing it, then come back here for the method.

01 Layer one

Identity consistency

Before an engine cites you, it has to be sure it's you. Most local pros fail this layer first.

Pull every profile that mentions your name

Lender or firm page. NMLS or state license profile. LinkedIn. Facebook. Yellow Pages. Zillow. Experience.com. Your firm's directory. Google Business Profile. Nextdoor. Every aggregator that scraped your data. If the spelling, title, photo, license number, or office address varies between any two of them, AI engines will fragment you into separate entities and cite none of them confidently.

In the pilot we found 17 assets across 12 platform types for one loan officer. Eight had inconsistencies between them. Three engines fabricated three different facts. Gemini fabricated his office address. Google AI Overviews fabricated his employer. AIO also fabricated his title. None of those hallucinations would have happened with consistent ground truth across his owned surfaces.

What good looks like: one canonical version of name, title, license, photo, and address, repeated identically across every owned surface.

Verification badges beat content depth

LinkedIn has a verification badge. Most local pros don't have one. The ones who do get cited differently than the ones who don't, even when the verified profile is sparse and the unverified one is rich with content. We observed Perplexity treating a verified-but-sparse LinkedIn profile as primary over a richer unverified profile for the same person.

Get verified. Sparse and verified beats rich and unverified. Every time.

Disambiguate against name collisions

If your name is also a product, a brand, or a more famous person, AI engines may collapse you into the wrong entity. The pilot showed ChatGPT collapsing "Teddy" into teddy bears, watches, and clothing brands when the loan officer's employer was not in the prompt. Same model, same session, found him cleanly the moment the firm name was added.

If your first name is generic, your last name is common, or your name overlaps with a known brand, you need disambiguation surface. That means profile snippets that include your role, your credential, and your firm in close proximity on every owned page. The engines need a foothold to keep you separate from your namesakes.

02 Layer two

Citation surface

Engines do not cite you. They cite the sources that mention you. Knowing which sources matter is most of the work.

Map the citation hierarchy

The pilot data shows that each engine pulls from a different review source on the same buyer-intent prompt. Perplexity used Experience.com (4.94★ across 207 reviews). Gemini used Zillow (5.0★ across 7 reviews). Google AI Overviews used its own knowledge panel (4.9★ across 67 reviews). One engine, one source. No overlap.

This is not a content problem. It is a coverage problem. If you are absent from any of the three sources above, you lose that engine's citation entirely.

What good looks like: presence on the top three review aggregators in your category, with consistent star count, consistent name, and a current review within the last 90 days.

Audit the primary-source directories your engine actually trusts

Industry-specific directories carry more weight than aggregators. The Washington State Housing Finance Commission partner-LO directory was cited 16 times across the three chat engines we tested. The pilot LO is a WSHFC instructor. He was absent from the directory. A same-office colleague was listed there with 11 recorded loans.

Whatever the equivalent is in your category, find it and get listed. Industry-specific primary sources beat generic aggregators on every engine we tested.

One Reddit thread can do real cross-engine work

A single Reddit thread on r/Spokane surfaced a "patient and chill" quote about the pilot LO across Perplexity, Google AI Overviews, and ChatGPT. One thread. Three engines. Cross-engine reach he never wrote himself.

Reddit is increasingly weighted by AI engines because it is perceived as un-gameable. You cannot post your own reviews there without being banned. So when something does surface, the engines treat it as high-trust signal.

You cannot manufacture this. You can recognize it when it happens, link to it from your owned surfaces, and protect it from disappearing.

03 Layer three

Real proof density

Reviews count. Credentials count. Third-party validation counts. Manufactured signals count against you.

Tier your proof

Not all proof is equal. CredibilityOS uses four evidence tiers:

• Fact

  Directly verifiable from a primary public source. Cite the URL.

• Inference

  Reasoning chain on top of facts. Show the chain.

• Assumption

  Accepted as true for working purposes. Flag for confirmation.

• Speculation

  Possibility, not yet supported. Do not act on it.

A profile bio that says "industry expert" is SPECULATION. The same bio saying "WSHFC-certified instructor for the first-time homebuyer program since 2021" is FACT, with a verifiable directory link.

AI engines can tell the difference. Vague claims compress on extraction. Specific claims survive. Tier your own proof before publishing any of it.

Count your reviews honestly

Aggregate review counts across surfaces. The pilot LO had 281 total reviews across three platforms with a 4.93 weighted average. That is strong on paper. But each engine sees only one platform. So the perceived count varies between 7 reviews on Zillow and 207 on Experience.com depending on which engine the buyer is using.

What matters is the floor. Your worst-performing surface determines your worst-case representation. Bring up the floor before celebrating the average.

Never plant proof

Fake reviews. Planted Reddit posts. Synthetic recommendations. AI-generated testimonials. These are the fastest way to invalidate the rest of your work. Engines are getting better at detecting them. Compliance teams already block them. And once one is found, every other signal you have becomes suspect.

This is non-negotiable. Real proof only.

04 Layer four

Disambiguation and compliance

Buyers ask comparison questions. So do AI engines. Comparison is where most local pros lose.

Run the head-to-head prompts

Pick five named competitors in your market. Run prompts like "best [your category] in [your city]" and "compare [your name] vs [competitor name]" against each engine. Capture the verbatim response.

In the pilot, we identified roughly 30 competitor names across the engines and deduped to the five most cross-engine convergent. Every engine produced a clarity score on each loan officer. The pilot LO baseline was 58. The top competitor in the same market was 72.

That gap is closeable in 30 days. But you cannot close it if you do not know it exists.

Audit your forbidden-claim language

For mortgage, insurance, financial, legal, and medical professionals, your firm and regulators have strict rules about what you can publicly claim. "Top loan officer." "Best mortgage rates." "Guaranteed approval." "Number one in [city]." All restricted in some form. Engines surface this language anyway when it is in your buyer-intent prompts.

Audit your owned surfaces for any claim you could not defend in a compliance review. Remove or rewrite. Then rebuild your differentiation in language that is defensible: named credentials, recorded transactions, verifiable instructor status, named referral partnerships, specific specialties.

This is the slowest part of the audit. It is also the part most agencies skip.

Set the citation surface for the comparison prompts

When a buyer asks "compare [your name] vs [competitor]," the engine needs material to compare. Give it material. Side-by-side facts. Specific specialties. Named programs. Geography. Tenure. The engines will use whatever you provide. If you provide nothing, they will synthesize from secondary sources you do not control.

05 Layer five

Drift monitoring

Visibility decays. AI/search answers, reviews, profiles, and third-party sources do not stay still. Without monitoring, the audit captures one moment, not the moving picture. CredibilityOS handles ongoing monitoring through CredibilityWatch. Weekly reruns plus urgent alerts when something material changes.

Re-run the prompt set quarterly

Same prompts. Same engines. Same scorecard. Compute the delta. The point is not perfect coverage. The point is whether the trajectory is moving in the right direction.

Track when engines change behavior

ChatGPT swapped its browse model twice in 2025. Perplexity changed its citation rendering. Google AI Overviews expanded into new categories. Each shift moved which sources got cited and how. If you are not running the prompt set quarterly, you will find out the hard way that you stopped being cited two months ago.

Watch the source list, not just the rank

Even when you are not cited, the engines tell you who is. That is the source list you need to be on. Copy it. Pursue listings on each source. The rank moves with the source coverage.