What a $4,500 kickoff actually produces.
A redacted example of the research memo and prepped punch list a paying customer receives at the end of the three-week kickoff engagement.
1. Executive summary
For the queries a Honolulu med-spa shopper actually types into ChatGPT and Perplexity, your business does not appear in the AI answer. Three of your competitors do, consistently, across both engines and across both head-intent ("best med spa in Honolulu") and long-tail intent ("med spa Kakaako that does morpheus8") queries.
The cohort spans 40 distinct queries, 2,104 total citation slots captured, 2 of the 7 measurement engines (Perplexity sonar and ChatGPT search). The headline pattern: AI cites Honolulu med-spa firm-owned sites and third-party content at roughly 1:1 (57% competitor / 41% independent web), with the remaining 2% in review directories. Zero from Reddit. Zero from YouTube on ChatGPT search; 1% from YouTube on Perplexity. These shares are recomputed against the corrected source-type classifier shipped 2026-05-23.
The punch list at the end of this memo orders the actionable work by impact. Item 1 alone would put you on roughly one third of the queries currently being answered without you.
2. Methodology and scope
Forty hash-locked queries split into 16 head-intent ("best med spa in Honolulu", "top rated medical spa Honolulu", neighborhood scoping, service scoping) and 24 long-tail intent (open-late, weekend-open, service-combined, insurance-fit, neighborhood-plus-service, value-conscious, trust-signal queries). Each query ran three times per engine per day for noise control.
Engines measured
In a current kickoff: all 7 surfaces from day one. Five citation-grade engines (Perplexity, ChatGPT search, Gemini grounded, Microsoft Copilot via Bing, Google AI Overviews) and two model-knowledge engines (Claude, Gemma). The data shown in this example was captured from May-17-2026 seed runs on 2 engines (Perplexity sonar + ChatGPT search). The 7-engine version of this same shape would add five more per-engine rows below; see the bank-honolulu teardown for the published format.
What this engagement did not measure
- Voice AI surfaces (Siri, Alexa, Google Assistant). Out of scope until measurable through public APIs.
- AI ad placements. Out of scope until those formats are measurable.
- Geo variation outside Oahu. Citation patterns in other islands may differ.
- Long-term causation. We measured what AI cites. We did not test whether shipping the punch list causes citation movement. Monthly delta memo will report observed movement honestly.
3. The headline finding
You do not appear in the AI answer for 38 of 40 measured queries. The two queries where you do appear are exact-name matches ("[your business name] reviews", "[your business name] Honolulu"), queries a customer only types if they already know your name.
For every demand-shape query (a customer asking AI to find a provider in your category without naming you), you are absent. The competitors who do appear, consistently:
- Med Spa A: 88 citations across 18 of 40 queries
- Med Spa B: 88 citations across 16 of 40 queries
- Med Spa C: 58 citations across 14 of 40 queries
- Med Spa E: 53 citations across 12 of 40 queries
Three of those four are not the largest med spas on Oahu by revenue or chair count. They are among the businesses whose authority signals AI engines have learned to trust for this category. That distinction is the entire work of the engagement.
Note on the anonymization: the competitors in this public example are labeled Med Spa A through J because they are not NeverRanked customers and did not consent to appear in our public materials. A real customer's deliverable names every competitor in full, with domains. The named competitive map is the product. On a public page, the pattern is what carries.
4. Source-type distribution
Across 2,104 citations on the cohort, where AI engines pulled from:
| Source type | % of citations | Hosts driving it |
|---|---|---|
| Competitor (cohort med-spa sites) | 57% | Med Spa A through J and the longer cohort tail |
| Independent web | 41% | Vendor pages, blog posts, third-party content about med spas |
| Review directories | 2% | two category-specific directories |
| YouTube | <1% | Single tutorial channel (Perplexity only) |
| 0% | Not cited for this category in the 2-engine seed | |
| Wikipedia | 0% | Not cited (Bing typically surfaces Wikipedia for this category; would appear in a 7-engine kickoff) |
| Forum / Q&A | 0% | Not cited |
The non-obvious finding. The two review directories AI engines cite for Honolulu med spas are not Yelp, not Google, not Healthgrades, not RealSelf. They are two niche, category-specific directories most med-spa operators have never heard of. Claiming a profile on each costs nothing and would put you in a source class AI engines demonstrably trust for your category. The two directories are named explicitly in the real deliverable. They are withheld from this public example on purpose, because which specific directories AI trusts in a category is the proprietary finding the engagement is paid to produce.
The Reddit zero is also a real finding. Many med spas spend on Reddit-adjacent reputation management. For your category, on Perplexity, that spend produces no citation share. For comparison, cross-category aggregate data shows Reddit drives 20-35% of citations on Perplexity for B2C SaaS and consumer electronics categories.
5. Per-engine breakdown (2 of 7 engines from the seed data)
Perplexity (916 citations measured)
- 51% competitor (cohort med-spa sites), 46% independent_web, 3% review_directory, 1% YouTube.
- The only engine that cited YouTube at all in this category seed.
- More likely to surface neighborhood-specific results.
ChatGPT search (gpt-4o-search-preview, 1,188 citations measured)
- 62% competitor (cohort med-spa sites), 37% independent_web, 1% review_directory.
- Zero YouTube, zero Reddit.
- Notably heavier weighting toward the spa's own site (62%) than Perplexity (51%). The per-engine asymmetry that single-engine measurement misses.
Gemini grounded, Microsoft Copilot (Bing), Google AI Overviews
The current kickoff measures these from day one. The bank-honolulu teardown (/teardowns/bank-honolulu/) shows what the 7-engine version of this breakdown looks like in the published format. In banking, the per-engine spread between the strongest competitor-share engine (Perplexity, 75%) and the weakest (Microsoft Copilot via Bing, 0%) was 75 percentage points, a finding invisible to any single-engine measurement. Expect a similar shape for med-spa once the 7-engine kickoff version of this example ships.
Claude and Gemma (model-knowledge)
The current kickoff measures these from day one. They tell you what AI says about you when it cannot search, which is the baseline-mention layer behind every ungrounded AI conversation. In the bank-honolulu cohort, Claude and Gemma cited bank-owned sites 72% and 70% of the time respectively, meaningful brand-recognition presence even before web search.
6. Competitive gap analysis
For the 38 queries where you do not appear, the table below shows which competitors did. Frequency = number of distinct queries that competitor was cited on, out of 40.
| Competitor | Frequency | Engines |
|---|---|---|
| Med Spa A | 18/40 | Both |
| Med Spa B | 16/40 | Both |
| Med Spa C | 14/40 | Both |
| Med Spa D | 13/40 | Both |
| Med Spa E | 12/40 | Both |
| Med Spa F | 9/40 | Both |
| Med Spa G | 8/40 | Perplexity only |
| Med Spa H | 5/40 | Perplexity only |
| Med Spa I | 4/40 | Perplexity only |
| Med Spa J | 3/40 | Both |
Notable structural observations:
- The two market leaders (Med Spa A, Med Spa B) appear on roughly 40-45% of queries each, including the head-intent "best med spa in Honolulu" variants. Closing the gap to them is a multi-quarter authority-signal project, not a quick fix.
- Mid-tier competitors (Med Spa C, Med Spa D, Med Spa E) appear on 12-14 queries each. The gap to this tier is closeable in 3-6 months of consistent work on the punch list below.
- The long-tail mid-tier shows up on 3-9 queries each, often on highly specific intent shapes. These are where focused effort produces fast, measurable movement.
7. Prepped punch list, ordered by impact
Sequenced by ratio of likely citation-share movement to implementation cost. Item 1 is the highest-leverage single piece of work on the list.
Priority 1 · Claim and complete the primary category directory profile
Why: This is the single review-directory AI engines demonstrably trust for your category on Perplexity. Three competitors hold claimed, complete profiles. You do not. (The directory is named explicitly in the real deliverable. It is withheld here because the specific directory is the proprietary finding.)
What: Create the business profile, populate all fields (services, hours, location, photos, treatment list, certifications). Add aggregate review information that mirrors your actual Google Business rating.
Priority 2 · Claim and complete the secondary category directory profile
Why: Same logic as Priority 1. Smaller volume but same source-type weighting. (Also named explicitly in the real deliverable.)
Priority 3 · Audit authority backlinks for currency
Why: AI engines weight authority backlinks heavily in this category. Stale, broken, or redirect-chained backlinks signal lower trust. We checked your site's inbound link profile against the three leading competitors; you have several authority links that 404 or redirect through chains.
Priority 4 · Add neighborhood and intent specificity to service pages
Why: The four competitors who dominate the long-tail queries all have service pages that explicitly mention the Oahu neighborhoods served, the specific service variants offered, and the kind of customer the service is for. Your service pages are more generic and AI engines do not cite them for neighborhood-specific or intent-specific queries.
Priority 5 · Update Bing Business Profile
Why: Microsoft Copilot pulls heavily from Bing Business Profile data. Most med spas in Honolulu have no Bing Business Profile.
Priority 6 · Refresh meta descriptions on the four high-value service pages
Why: AI engines that grounded-search the web read meta descriptions as one signal of page topic. Your meta descriptions on the four highest-value service pages are either missing or generic.
Items 7 through 12 (lower priority) are listed in the appendix of the customer deliverable. Five additional moves with the same effort and window estimates.
8. What happens next (in a real engagement)
- Day 1 of ongoing: daily measurement across the seven AI surfaces begins. Brief daily status email confirms runs completed.
- End of month 1: first monthly delta memo. What moved, what did not, why we think it moved, any punch list updates.
- Ongoing: punch list refreshes as your team ships items and as AI engines shift. Drift detection flags cohort-wide pattern changes affecting your numbers.
How to scope this for your category
Email lance@neverranked.com with the category you want to measure and three to five competitors you would want on the cohort. The free 1-page diagnostic runs 5 real buyer questions for your category across all 7 AI tools and shows you which competitor firms AI is currently naming. One per business.
Full engagement is $4,500 to kick off a category, $1,500 a month after for ongoing measurement. Per category, not per client. Month to month after the kickoff.