Your customers are asking the same question to find a business like yours.
A Google search gives you ten links. An AI answer gives you three names. You can check a Google rank any time. You cannot see an AI answer that left you out.
NeverRanked measures what the AI answer engines cite for your category, split across two layers that fail in different ways.
The deliverable is a forensic research memo and a prepped punch list, ordered by impact. Your team executes it. We do not. That separation is structural.
Atlas is the data-interpretation layer of your dashboard. It answers what the measurement shows. It refuses to tell you what to do. That separation is the engagement.
The boundary is structural. Prioritization lives in your monthly memo, written by the principal. Atlas holds the data. Crossing that line would damage the engagement.
This one is built to be. Four reasons the numbers can be trusted.
Every methodology claim is anchored in a hash-locked pre-registration before the test runs. The claim cannot move after the data lands.
The measurement and aggregator code is on GitHub. One of the seven engines, Gemma, is open-weight, so your own analyst can re-run the prompts and reproduce the numbers without us in the room.
Seven surfaces measured every day. An AI answer can change between two askings of the same question, so a single snapshot is not measurement.
We never touch your website, your code, or your CRM. No integration, nothing installed, nothing to breach. We measure the public engines from outside. It is a research engagement, not a software install.
Five stages. Plain words. No SaaS dashboard between you and the work.
Lock the category, the cohort, and the 18 buyer questions we will measure. One call, no homework.
See an example question set →We measure daily across 7 AI tools. Same questions, same hash, every run apples to apples.
PDF or markdown to your team. Named competitors, observed gaps, the clear list of what to fix first.
You ship the work. We measure whether it lands. That separation is the whole position.
What moved, what did not. Updated punch list. Drift alerts when a competitor moves in your category.
The first research memo arrives three weeks after the scoping call.
Three published examples of what an engagement produces
Six measurements across five Hawaii categories plus the first cross-geo (Austin TX CPAs). The Claude collapse generalizes; the Gemma collapse does not.
21-bank cohort. The Microsoft Copilot gap no single-engine measurement catches.
46-practice cohort. Zero practice-owned websites cited by Microsoft Copilot.
Every teardown is built from a hash-locked question set, 3 measurement runs, and the same 7 AI tools. Anonymized at the firm level for non-customers, named in full inside paid engagements. The Austin CPA teardown is the first non-Hawaii measurement, built specifically to test whether the Hawaii CPA training-data findings generalize across geographies. The cross-geo finding splits the original pattern.
Hawaii CPA Claude own-share: 1%. Austin CPA Claude own-share: 0%. Hawaii CPA Gemma own-share: 2%. Austin CPA Gemma own-share: 23%. Same category, same query shape, two different geographies. The Claude collapse generalizes. The Gemma collapse does not. This is the kind of distinction single-geo measurement cannot surface. Read the cross-geo teardown →
21-bank cohort. The widest cross-engine gap of any category measured. One bank owns the head queries on training-data tools; the long tail sits open on Copilot.
42-firm cohort. AI defers to lead-gen aggregators (SmartAsset, Unbiased, Plannersearch) more than to any individual firm. The structural ground for firms sits outside that middleman tier.
46-practice cohort. Microsoft Copilot cites zero practice websites for the entire cohort. The first Honolulu practice that ranks first on Bing organic effectively owns the Copilot answer.
33-firm cohort. The dominant firm gets roughly three times the citations of the second-tier firms. For any firm outside the top 5, the closable ground is the long tail and Microsoft Copilot.
41-firm cohort. Claude and Gemma cite Hawaii CPA firms less than 2% of the time. Competitive game plays inside OpenAI, Gemini, Perplexity, and Google AI Overviews.
37-firm Austin cohort, 7,738 measured citations. First non-Hawaii measurement, built to test whether the Hawaii CPA training-data findings generalize. The cross-geo finding splits the original pattern: Claude's collapse holds across geographies, Gemma's does not. A category pattern from one geo measurement was actually two patterns once measured in two geos.
The cross-category teardown reads all six measurements against each other →
Which questions in your category get answered, who gets cited when you do not, and which kinds of sources the engines actually pull from when they decide.
Take one named reference. At Hawaii Theatre Center, a forensic readout surfaced what a standard scan walks past: a Charity Navigator profile not updated since 2023, a Better Business Bureau profile last touched in 1999, a missing Bing Business Profile, authority backlinks pointed at the wrong places. The quiet, citation-shaping detail nobody is looking at.
No bundled tiers, no per-seat math.
Free 1-page diagnostic before you commit. We run 5 real customer questions for your category across all 7 AI tools, then send a 1-page snapshot showing which competitors AI is naming and whether you’re one of them. One per business. The full engagement adds the rest of the question set (18 per category), weekly tracking, a cohort baseline that makes the numbers mean something, and a clear list of what to fix.
NeverRanked is a research practice, not a software company. The measurement, the memos, and the punch lists are produced by Lance Roylo, in Honolulu. There is no account layer between you and the person doing the research.
How the practice operates: we measure, we do not execute. We report what the AI engines actually cite, never what we claim our work caused. We do not promise a citation lift in advance. A finding that cannot be substantiated does not ship. That discipline is the product.
Lifted from the inbound emails Lance answers most often.
SEO measures search engine ranking factors on your own site. We measure what AI tools actually cite when buyers ask category-shaped questions, across 7 surfaces. The deliverable is also different: SEO tools give you a dashboard to interpret yourself; we hand off an interpreted research memo plus a prepped punch list your team executes. See /vs/ for the structural comparison.
Two reads on this. First, AI search usage in B2B and high-consideration consumer decisions is already non-trivial and growing on the curve we have public visibility into. Second, even if your buyer is not asking ChatGPT today, your competitor showing up there first when they do is the move you can not undo. We measure that surface so you know whether the move has already started.
We do not promise a lift in advance. The only promise we make is the measurement itself: you will know what AI cites for your category, what gaps exist, and what conditions a buyer of your category typically closes to move the needle. Whether your team executes the punch list well is what determines lift, and we measure that monthly so the answer is observable, not asserted.
We measure. We do not execute. We do not write content, edit pages, deploy schema, update profiles, or change your site. Your in-house team or your agency executes against the punch list we deliver. That separation is structural and is the whole position. It also means we never compete with your agency for execution hours.
Two ways. The instant self-serve check at check.neverranked.com tells you what the 7 AI tools can read from your site. The hand-built 1-page diagnostic runs 5 real customer questions for your category across all 7 AI tools and sends a snapshot showing which competitors AI names and whether you are one of them. One free diagnostic per business.
More: the full FAQ covers cancellation, NDAs, agency channel, data handling, and what happens if a finding turns out to be wrong.