NeverRanked · AEO for Hawaii CPA firms

The web-searching engines find you. The training-data engines do not.

Across the Hawaii business categories we have measured, Hawaii CPA firms produce one of the most distinctive AI-citation patterns. OpenAI cites Hawaii CPAs 60% of the time. Claude cites them 1%. For your category, the competitive game plays inside 4 specific engines.

The headline finding for your category: Hawaii CPA firms produce a striking training-data pattern. The web-searching AI engines (ChatGPT search, Gemini grounded, Perplexity, Google AI Overviews) reach between 45% and 60% firm-owned citation share. The training-data AI engines (Claude, Gemma) cite Hawaii CPA firms 1-2% of the time. In several categories (banking, wealth, dental, law) the training-data engines are a high own-share tier. For Hawaii CPAs they collapse to nearly zero, as they also do for med spas, HVAC, and Austin CPAs. The structural read: Hawaii CPA firms have substantially less category-level editorial presence than the equivalent firms in adjacent professional services. The competitive game for AEO plays inside the 4 web-searching engines.

The cross-category picture

Category	Firm-owned share%	Claude own%	Gemma own%
Hawaii consumer banking	53%	88%	70%
Hawaii wealth management	47%	38%	68%
Honolulu dental practices	44%	34%	58%
Hawaii law firms	39%	51%	78%
Hawaii CPA firms	39%	1%	2%

Hawaii CPA firms tie with law firms on firm-owned citation share (both 39%). The divergence is in the training-data row. Hawaii law firms have decades of editorial coverage (Chambers, BestLawyers, named-attorney bylines) feeding the training-data engines. Hawaii CPA firms do not have an equivalent editorial layer. CPA work is more transactional, less likely to surface in profile pieces, less likely to generate named-partner press coverage. The training-data engines reflect that. The full Hawaii CPA teardown covers the 41-firm cohort, the 5,809 citations captured, and the two-axis framework that explains the divergence.

What this means for an operator

For a Hawaii CPA firm, the closable competitive ground sits inside the four web-searching engines (OpenAI, Gemini grounded, Perplexity, Google AI Overviews). They produce the vast majority of firm-owned mentions in the data, while the training-data engines contribute almost none. A firm's website, schema deployment, and editorial presence on Hawaii-CPA-specific surfaces (HSCPA member directory, Clutch B2B profile, named-partner LinkedIn presence) all move citation share inside these engines.

Microsoft Copilot is the universal cohort-wide gap (2% own-share, consistent with every other category we measured). The Copilot read for Hawaii CPAs: in our data, the firm ranking first in Bing organic for category queries is the one Copilot tends to surface, while firms absent from Bing organic are absent there too.

Training-data engines (Claude, Gemma) are a category-wide blind spot no individual firm can close in the short term. Closing the gap would require category-level editorial momentum (years of named-partner press coverage, sustained editorial surface in publications that AI training data ingests). The honest read for an operator: do not invest in closing the training-data gap as a near-term AEO move. Compete inside the web-searching engines where the surface is reachable.

What an engagement looks like for a Hawaii CPA

The standard engagement shape, scoped to a CPA firm:

Scoping call (30 min). Lock the 18 buyer questions a real Hawaii CPA buyer would ask AI. Lock the cohort (the firms you compete with for buyer attention, including specialty firms in your service variants). Confirm the questions reflect your specific practice mix (tax planning, audit, advisory, nonprofit, fractional CFO, etc.).
Three-week kickoff. Daily measurement across all 7 AI tools.
Research memo + prepped punch list. Named competitors, observed gaps per query, per-query playbooks for the weakest queries, schema and page-template recommendations for what your team or agency ships, agency-ready SOW scope per move.
Monthly delta memo after the kickoff. What moved, what did not, drift alerts when a competitor moves on your category.

For Hawaii CPAs specifically, the punch list weights toward the 4 web-searching engines: service-specific landing pages, HSCPA directory profile completion, Clutch B2B profile completion, and the Microsoft Copilot first-mover push via Bing organic. The training-data axis is treated as a structural feature of the category, not a target.

See how 7 AI tools read your firm’s site right now

Free check. Enter your URL and we will show you what AI tools can read from your site, what they cannot, and what is making you harder to find. No signup.

Run the AI search check Get the 1-page diagnostic

Free check is automated and instant. The 1-page diagnostic is hand-built and shows which competitor firms AI is naming for your specific buyer questions.

Honest scope

41-firm cohort, expanded across 3 measurement runs. Cohort built in 4 passes: 5 anchor firms registered before run #1, then 15 + 8 + 11 additional firms surfaced via cohort-coverage scans after each run. The full firm list is anonymized in the public teardown (non-customer anonymization rule).
Hawaii-specific. National Big-4 firms (KPMG, PwC, Deloitte, BDO, CLA) are excluded from the cohort to keep the comparison Hawaii-specific. They appear as third-party content occasionally.
Measurement-only. We do not write content, edit pages, claim profiles, or change your site. Your team or your agency does. That separation is structural and is the engagement.
The two-axis pattern is documented in the cross-category teardown. Hawaii CPA's training-data collapse is currently the strongest evidence for the two-axis framework. Confidence is low (n=1) and will grow as we measure CPA firms outside Hawaii.

NeverRanked home · Full Hawaii CPA teardown · Cross-category two-axis framework · How we measure