The 75-point engine spread that no single-engine tool can see.

First empirical teardown from the 7-engine NeverRanked methodology. 21-bank Hawaii consumer banking cohort, 18 hash-locked queries, 3 usable runs, 5,153 citations captured. Pattern-readiness cleared. Subject brand anonymized; cohort named in full.

The cohort (named)

21 Hawaii financial institutions AI engines cite for consumer banking queries in this category. Naming the cohort is honest — these are all publicly competing brands, and the methodology demonstration value comes from the pattern across the group, not from singling any one out.

Major banks: Bank of Hawaii (boh.com), First Hawaiian Bank (fhb.com, locations.fhb.com), American Savings Bank (asbhawaii.com), Central Pacific Bank (centralpacificbank.com, cpb.bank), Hawaii National Bank (hawaiinational.bank, ebanking.hawaiinational.com), Territorial Savings (territorialsavings.com, territorialsavings.net).

Credit unions: Hawaii USA FCU (hawaiiusafcu.com), Hawaii State FCU (hawaiistatefcu.org, hawaiistatefcu.com), HFS FCU (hfsfcu.org), Pearl Hawaii FCU (pearlhawaii.com), University of Hawaii FCU (uhfcu.com), Hawaii Community FCU (hicommfcu.com), Hawaii FCU (hawaiifcu.org), Big Island FCU (bigislandfcu.com), CU Hawaii (cuhawaii.com), Hawaiian Financial FCU (hificu.com), HLE FCU (hlefcu.com).

Methodology summary

7 AI surfaces measured, all on the same day, all on the same query set:

• 5 citation-grade engines (search the web at query time): Perplexity, ChatGPT search (gpt-4o-mini-search-preview), Gemini grounded (gemini-2.5-flash with googleSearch tool), Microsoft Copilot via Bing organic SERP, Google AI Overviews.
• 2 model-knowledge engines (answer from training data, no web search): Claude (claude-haiku-4-5), Gemma (google/gemma-4-31B-it via Together AI, open-weight).

18 queries hash-locked at 48b6c76f.... 3 repetitions per query per engine for noise control. 3 usable runs all on 2026-05-23, totaling 1,134 successful API calls and 862 cited-text rows scanned. Pattern-readiness rule of 3+ usable runs is cleared.

Full methodology + open-source measurement code at /methodology/.

Source-type distribution (cohort-wide)

Across 5,153 citations captured over 3 hash-locked runs, where AI engines pulled from for Hawaii consumer banking queries:

The cohort-wide read. For Hawaii consumer banking, AI cites bank-owned sites slightly more than third-party content (53% vs 42%). That's not the result a buyer expects. Conventional SEO instinct says "AI cites third-party content about you, so optimize the content, not the site." For Hawaii banking, the data says the opposite is closer to true: own-site optimization is the majority of where AI is looking, third-party content is the minority. The actionable shape of the punch list changes accordingly.

But the cohort-wide number hides the bigger finding. The per-engine breakdown is where the moat lives.

Per-engine breakdown — the 75-percentage-point spread

Other per-engine findings worth naming

• Google AI Overviews is the only engine that surfaces Reddit (7%) and social (4%) for banking queries. Editorial guidance on those surfaces won't help with AIO presence; user-generated content can.
• Reddit, Wikipedia, YouTube, social, and review-directory citations are essentially absent from the other six engines for this category. Banking is not a forum-driven category in AI answers. (Compare to consumer electronics or software categories, where Reddit shows up at 15-30% in AI Overviews and Perplexity.)
• Claude and Gemma (model-knowledge engines) cite the major Hawaii banks by name with no web search at all. 72% and 73% competitor share respectively, just from training data. That means even before any AI search happens, these banks have measurable model-knowledge presence. No comparison tool measures this layer.
• ChatGPT search surfaces location-specific URLs heavily. The host locations.fhb.com alone accounts for 54 of OpenAI's 60 First Hawaiian Bank citations — OpenAI strongly favors specific branch/location pages over brand home pages for "best bank in [neighborhood]" queries. Actionable optimization finding.

Top cited brands — mentions across engines

Brand-host mentions across the 862 cited-text rows, with position-in-answer breakdown (Q1 = lead position, Q4 = tail). Top 10 by mentions:

What the position-bias column reveals. Bank of Hawaii is "lead-heavy" — 79% of its 612 mentions are in the early half of AI answers (Q1+Q2). That's the dominant AI-recommendation pattern. Hawaii National Bank, with notably less total visibility (148 mentions), is "mid-heavy" — 53% Q3 alone. Same engine, same query universe, fundamentally different competitive posture. Total mention count alone misses this; position bias is the depth signal no comparison tool surfaces.

True third-party citation sources (the actionable list)

After cohort classification, the third-party content AI cites for Hawaii consumer banking, ranked by recurring-host frequency across the 3-run dataset:

These are the surfaces where editorial mentions actually move citation visibility for Hawaii consumer banking. Notice the engine-specific patterns: Reddit threads matter for Google AIO and Gemini specifically (invisible to the other 5 engines). Wikipedia and Yelp matter for Bing/Copilot specifically. Social platforms (Facebook, Instagram, TikTok) only show up in Google AI Overviews. The path to AI presence in each engine is structurally different, and the strategy for moving citation share has to be specific to which engines matter to the bank's actual buyers.

What this teardown does not yet include

Why this is anonymized

The cohort is named because these are publicly-competing brands appearing in any local "best Hawaii bank" comparison; naming them collectively is honest. The teardown does not single out one bank as a "subject" of analysis on the public version, because publicly naming a specific brand alongside competitive findings creates trademark, trade-libel, and endorsement-implication exposure that does not exist for the cohort-level analysis above. The unredacted version — subject-named with the full per-brand competitive analysis — is available 1:1 to qualified prospects in conversation. Same anonymization pattern is used across NeverRanked's public artifacts.

Methodology + reproducibility

Full methodology at /methodology/. Public source code (the measurement runner, the source-type classifier, the within-citation analyzer, the aggregator) at github.com/LanceRoylo/neverranked-outreach. Gemma is open-weight — an auditor can independently re-run the same prompts and verify the model-knowledge layer numbers.

Raw data manifests for the 3 runs that produced this teardown are at dryrun/out/2026-05-23T10-13-34Z/ (2-engine seed), dryrun/out/2026-05-23T11-14-31Z/ (7-engine pass), and dryrun/out/2026-05-23T21-42-59Z/ (third 7-engine pass clearing pattern-readiness). All three runs aggregate cleanly into the same query-set cohort.

If you're a Hawaii financial institution evaluating this methodology

Email Lance@hi.neverranked.com for the unredacted version with the specific subject-bank's competitive position, or to scope a full kickoff engagement on your own brand. Engagements are $4,500 kickoff per category, $1,500/month ongoing.

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