Local AI Registry

Methodology

How the scoreis built.

Eight signals. One number from 0 to 100. The same algorithm for every business. The heaviest signal is whether AI can retrieve you at all — because that is the literal input to every recommendation. No money moves the score.

08
Signals scored
100
Point scale
26%
Weight on retrievability
0
Paid placements, ever

The thesis

One number. One mechanism.

AI assistants don’t recommend the closest business, the prettiest website, or whoever paid the most. They recommend a business they can retrieve and recognize as real: one that many independent, trustworthy sources describe the same way.

Because retrieval is the gate everything else passes through, the single heaviest signal is whether you appear in the results an AI model pulls from when it answers. Everything else either earns that retrievability or refines what the AI says once you’re in.

Retrievability isn’t one ranking factor among many. It’s the precondition for all of them.

How a recommendation is assembled

“best med spa near me”

med spa Irvinebotox payment plansskin clinic reviewsaesthetic nurselip filler near meYouTube review“Best of OC” listGoogle profileYelp pagenews mentionQUIKTOX Wellness

The weights

How much each signal weighs.

Eight signals, identical for every business, ordered by how much each one moves an AI recommendation. The single heaviest is whether you can be retrieved at all.

Retrievability and the attestation that earns it are the retrieval tier — together 46% of the score. Nearly half.

Retrieval tier
26%Retrieval grounding
20%Off-property attestation
14%AI visibility
12%Reviews & response
8%Backlink & citation authority
8%Google Business Profile
7%Speed & mobile
5%Content freshness & schema

The formula

Eight signals become one number.

We don’t average them. The method leans on the weakest signal, so a strong one can’t cover for a poor one.

  • Retrieval grounding72
  • Off-property attestation65
  • AI visibility48
  • Reviews & response80
  • Backlink & citation authority55
  • Google Business Profile90
  • Speed & mobile68
  • Content freshness & schema75
67.1AI Score

Lose retrieval, and the score follows.

Retrieval is the heaviest signal. Drop it from 72 to 20 and the score falls from 67 to 48. A simple average would still read 55. Strong signals would be hiding a weak one.

AI Score = 100 · exp( Σ wᵢ · ln xᵢ )

The eight signals

Every signal, in detail.

Retrieval tier

Retrieval grounding

Whether you sit inside the ranked set an assistant actually retrieves and cites. Measured across AI-Overview citations, organic top-10 presence for your money queries, and the Bing index ChatGPT leans on.

Top-10 coverage across the keyword setAI-Overview citation rateBing top-10 overlap

sim(q, p) = (E_q · E_p) / (‖E_q‖ ‖E_p‖)

Ahrefs: 38–76% of AI-Overview citations come from organic top-10; ChatGPT matches Bing’s top results ~87% (Seer).

RETRIEVED BY AI · TOP 10123456This business78910rank 11+ · never seen

Retrieval tier

Off-property attestation

What others say about you off your own website. This is the attestation that trains retrievers to surface you. Volume, consistency, and source quality across mentions.

YouTube mention strengthBranded web mentionsThird-party “best-of” inclusion

Ahrefs (75k brands): YouTube mentions correlate ρ≈0.74 with AI visibility and branded mentions 0.66–0.71, far above backlinks at 0.22. Correlation, not causation.

YouTube review“Best of” listNews mentionForum threadThis business

Recognition tier

AI visibility

Whether the major assistants name you when asked the questions your customers ask, and whether they name you for the service you actually want to be found for.

Named-by-assistant rateService-match accuracySentiment of the mention

Probed monthly across ChatGPT, Gemini, Perplexity, and Google AI Overviews.

ChatGPT72%named-by ratelast 3 probesPerplexity64%named-by ratelast 3 probesGemini18%named-by ratelast 3 probesAI Overviews55%named-by ratelast 3 probes

Recognition tier

Reviews & response

Volume, velocity, the specificity of review language (“great with anxious dogs” beats “5 stars”), and how consistently you respond. AI quotes named services, not star counts.

Review count (log-damped)90-day velocitySpecificity score% responded

specificity = entity/keyword density of review text

Whitespark 2026: review signals ≈16% of the local pack and rising.

vaguespecificTHIS BUSINESS

Recognition tier

Backlink & citation authority

Where you’re named across the web and how authoritative those sources are. A citation in the New York Times is worth a thousand on a link farm.

Domain authorityEditorial-quality classifierContextual relevance

Weighted by domain authority and editorial quality, not raw link count.

domain rating →citations →one strong citation

Technical tier

Google Business Profile

Completeness of every required and recommended field, plus how fresh it is. A complete profile untouched for two years scores worse than an incomplete one updated last week.

Required fieldsRecommended fieldsUpdate cadence

Whitespark 2026: GBP signals ≈32% of the local pack.

90%FIELDS COMPLETErequired · recommended · fresh

Technical tier

Speed & mobile

How fast and stable your site feels on a real phone, at the 75th percentile of real users, against the thresholds Google publishes.

LCP ≤ 2.5sINP ≤ 200msCLS ≤ 0.1

Core Web Vitals thresholds per web.dev (LCP 2.5s · INP 200ms · CLS 0.1).

LCPthresholdpassINPthresholdoverCLSthresholdpass

Technical tier

Content freshness & schema

Whether the site is current and answers the questions people ask now. Also the technical plumbing that lets machines read it cleanly: valid structured data, canonicals.

Crawl recencyIntent alignmentValid JSON-LD + canonicals

Every business website re-crawled on a 28-day cadence; schema validated continuously.

13 weeks agothis week28-DAY CADENCE

Methods referenced

We didn’t invent the maths.

Every part of the score is a named, published method, borrowed from information retrieval, index construction, and game theory, and each one is cited.

Fig. 8 — Methods composing the AI Score · n = 10 · 3 stages · full citations

Retrieve & rank

RAG

p(y|x) ≈ Σ pη(z|x)·pθ(y|x,z)

Defines why retrievability is the gate: the answer is written over the top-k retrieved documents.

Lewis et al., NeurIPS 2020

DPR + cosine

sim(q,p) = E_q·E_p / ‖E_q‖‖E_p‖

Dense dual-encoder retrieval; cosine similarity models query–business relevance.

Karpukhin et al., EMNLP 2020

BM25 / TF-IDF

Σ IDF·tf(k₁+1) / (tf + k₁(1−b+b·|d|/avgdl))

The lexical baseline behind literal-match retrievability.

Robertson & Zaragoza 2009 · Spärck Jones 1972

LUKE (entity-aware)

h_e = softmax(Q_eK_eᵀ/√d)·V

Why precisely described entities are retrieved over generic pages.

Yamada et al., EMNLP 2020

PageRank

PR = (1−d)/N + d·Σ PR(j)/L(j)

Classic link authority. Now one input among many, and the foil for entity retrieval.

Brin & Page, 1998

Fuse & aggregate

Reciprocal Rank Fusion

RRF(d) = Σ 1 / (k + rᵢ(d)), k = 60

Fuses multiple ranked signal lists into one composite (k = 60).

Cormack, Clarke & Büttcher, SIGIR 2009

Weighted geometric mean

100 · exp(Σ wᵢ·ln xᵢ)

Aggregation that penalises imbalance and enforces the heaviest signal.

UNDP Human Development Index, 2010

Composite-index method

normalise → weight → aggregate → test

The normalise → weight → aggregate → sensitivity-test spine.

OECD/JRC Handbook, 2008

Attribute & render

Shapley values

φᵢ = Σ |S|!(n−|S|−1)!/n! · [v(S∪i)−v(S)]

Attributes each signal’s contribution to a business’s final score.

Shapley, 1953

Force-directed layout

F = k²/d (repel) − d²/k (attract)

Renders the knowledge-graph visuals.

Fruchterman–Reingold, 1991

The state of the market

Most aren’t seen.

Each mark is a local business. The bright ones can be named by an assistant today; most can’t. The score is how a business moves from unseen to named.

A dusk-lit 3D map of Orange County. A score bubble over each metro marks how often AI assistants name a local business there.

Orange County

Every block has an AI Score.

It’s how often an assistant can name a business there when a customer asks. It is not the same across the county.

South Coast Metro has the highest median AI Score in the county, at 74.

illustrative · © Mapbox © OpenStreetMap

Editorial independence

Five promises we keep.

A score is only worth something if nothing can buy it. These five hold the method honest.

Money never moves a score.

Not from the businesses we list, not from agencies, not from anyone.

Rankings are earned.

No promoted entries in any leaderboard. Every ranking is computed, then audited by a person.

Links go where they say.

No affiliate links, no monetized click-outs. We earn nothing when you follow one.

Every input is public.

Each signal that moves the score is published in full, and we date every change.

One honest category per business.

The one its customers would name first. We check for miscategorization every month.

We stand behind every score.

Reading a business page

What the marks on a business page mean

Every fact on a business page carries one of three marks, and every mark points to a reference at the end of that page.

Confirmedsolid circle
From a known source: the business's website, its Google Business Profile, review sites like Yelp, Healthgrades and RealSelf, a licensing board, or measured by Local AI Registry.
Visitor submittedhollow circle
Added or corrected by a person, with the date and how they know, like a phone call or a visit.
AI guesshollow triangle
What AI engines like ChatGPT, Gemini and Perplexity are saying right now. Nobody has confirmed it.

How AI reads a business

How AI decides what to tell you about a med spa

When you ask ChatGPT, Gemini or Google for a med spa, the AI doesn't visit each business. It reads what's already written about it: the business's website, its Google Business Profile, review sites like Yelp and RealSelf, news stories, licensing boards and directories like ours. Then it repeats what those sources agree on.

That's why AI gets small facts wrong. If Yelp says a place is open Mondays and the website says it isn't, the AI has to pick one. Each business page on the registry lists the answers that disagree with what we've confirmed.

The more sources agree, and the more specific they are, the more confidently AI recommends a business. A review that says "the nurse did half a syringe in my lips and it looks natural" is far more useful to an AI than one that says "great place."

How AI decides what to tell you about an eye surgery practice

When you ask ChatGPT, Gemini or Google for an eye surgeon, the AI doesn't visit the practice. It reads what's already written about it: the practice's website, its Google Business Profile, review sites like Healthgrades and Yelp, your state's medical and optometry boards, news stories and directories like ours. Then it repeats what those sources agree on.

That's why AI gets small facts wrong. If one site says a surgeon still operates there and the practice's own website doesn't list them, the AI has to pick one. Each business page on the registry lists the answers that disagree with what we've confirmed.

The more sources agree, and the more specific they are, the more confidently AI recommends a practice. A review that says "my surgeon did my cataract surgery on a Tuesday and I was driving by Friday" is far more useful to an AI than one that says "great doctor."

How AI decides what to tell you about a local business

When you ask ChatGPT, Gemini or Google about a local business, the AI doesn't visit it. It reads what's already written about it: the business's website, its Google Business Profile, review sites, news stories and directories like ours. Then it repeats what those sources agree on.

That's why AI gets small facts wrong. If Yelp says a place is open Mondays and the website says it isn't, the AI has to pick one. Each business page on the registry lists the answers that disagree with what we've confirmed.

The more sources agree, and the more specific they are, the more confidently AI recommends a business. A review that names who helped and what they did tells an AI far more than one that says "great place."

Before you choose

How to choose a med spa, with or without AI

A few checks take ten minutes and matter more than any rating. This is general information, not medical advice.

  1. 01Look up every injector. Doctors are listed by your state's medical board, and nurses and nurse practitioners by its nursing board. Both lookups are free.
  2. 02Ask who does the injections, and check that person's license.
  3. 03Ask for the price per unit of Botox and per syringe of filler, not per area.
  4. 04Read reviews that name a provider and a treatment. Vague praise is easy to fake.
  5. 05Check that the hours and phone number match on Google and the website. Mismatches are how people end up at a locked door.
  6. 06Ask whether they dissolve filler and when they would tell you not to add more.

How to choose an eye surgery practice, with or without AI

A few checks take ten minutes and matter more than any rating. This is general information, not medical advice.

  1. 01Look up the surgeon who'll operate on you. Your state's medical board lists every ophthalmologist, and its optometry board lists the optometrists who do your exams and follow-ups. Both lookups are free.
  2. 02Ask who does the surgery itself and who sees you before and after it. At many practices those are different people.
  3. 03Ask which laser or lens they use for your procedure, and why it suits your eyes.
  4. 04Ask what the consult covers, how long it takes, and whether your eyes will be dilated so you need a ride home.
  5. 05Ask what your insurance pays for and what it leaves you to pay. Cataract surgery is often covered and LASIK usually is not.
  6. 06Read reviews that name a surgeon and a procedure. Vague praise is easy to fake.

How to choose a local business, with or without AI

A few checks take ten minutes and tell you more than any rating.

  1. 01Check that the hours and phone number match on Google and the business's own website. Mismatches are how people end up at a locked door.
  2. 02Read reviews that name a person and what they did. Vague praise is easy to fake.
  3. 03Look for recent reviews and recent posts. A page nobody has touched in a year may not match the business today.
  4. 04Ask for the price before you go, and ask what it includes.
  5. 05Where the work needs a license, ask whose license it is and look it up with your state.

Find out what AI says about you.

And it’s free.

Appendix · References

Every method, cited.

The published work the AI Score is built on. None of it is ours to claim.

  1. 01

    Lewis, Perez, Piktus, et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS.

  2. 02

    Karpukhin, Oğuz, Min, et al. (2020). Dense Passage Retrieval for Open-Domain Question Answering. EMNLP.

  3. 03

    Robertson & Zaragoza (2009). The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval.

  4. 04

    Spärck Jones (1972). A Statistical Interpretation of Term Specificity and Its Application in Retrieval. Journal of Documentation.

  5. 05

    Yamada, Asai, Shindo, et al. (2020). LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention. EMNLP.

  6. 06

    Brin & Page (1998). The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems.

  7. 07

    Cormack, Clarke & Büttcher (2009). Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods. SIGIR.

  8. 08

    United Nations Development Programme (2010). Human Development Report 2010 — Technical Notes. UNDP.

  9. 09

    OECD & Joint Research Centre (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Publishing.

  10. 10

    Shapley (1953). A Value for n-Person Games. Contributions to the Theory of Games, Vol. II.

  11. 11

    Fruchterman & Reingold (1991). Graph Drawing by Force-Directed Placement. Software: Practice and Experience.

New to the registry? You can also read the Local AI Registry launch announcement, or read The Convergence, our white paper on how local businesses win AI recommendations.