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Why this matters
Search is shifting from a list of blue links to a single, confident answer. When a prospective client asks an AI assistant to "find me a fee-only financial advisor near me," the assistant names a handful of firms — and many people never look past them.
That creates a new, largely invisible competitive layer for RIAs. A firm can rank well on Google and still be absent from every AI recommendation. This leaderboard exists to make that layer measurable — so firms, and the people researching them, can see who actually shows up.
A new front door
AI assistants increasingly sit between a prospect's question and your firm's website. Being named is the new "page one."
Short lists, not ten links
Assistants surface a handful of firms, not pages of results — so presence on that list carries outsized weight.
Hard to observe
Answers are probabilistic and vary run to run, so no single query tells the full story. We measure at scale, on a fixed query set.
Actionable
Unlike a black-box ranking, visibility responds to the content, citations, and clarity a firm controls.
Key metrics
The leaderboard is rebuilt every edition from a fixed query set run across every market and every assistant we track.
How scores work
Every firm receives an AI Visibility Score from 0 to 100 — a bounded measure of how often and how prominently it appears when people ask AI assistants for advisor recommendations.
The three inputs
The score blends per-platform performance, measured across every query and market:
01 · Reach
How often the firm is named
The share of relevant queries that surface the firm at all. The single largest component of the score.
02 · Position
Where in the answer it lands
Being recommended first counts for far more than a passing mention at the end of a list — #1 mentions vastly outweigh #15.
03 · Usage weighting
Where consumers actually ask
The four platform sub-scores blend by real US platform usage — ChatGPT 63%, Gemini 20.8%, Claude 14.5%, Perplexity 1.7% — so visibility on high-usage platforms counts most.
One number, three forms
Score, grade, and rank agree
Rank orders firms by score; the grade is the same score banded on fixed thresholds. They can never tell different stories.
Weight source: US web-visit share May 2026 (Similarweb via TechCrunch/First Page Sage), normalized over the four measured platforms; reviewed quarterly.
Scores are absolute, not curved. Sub-scores band each platform's reach and position on fixed thresholds, so a 72 next quarter means the same thing a 72 means today — and the #1 firm isn't automatically a 100. Because rates replace raw counts, scores stay comparable as coverage grows.
Visibility grades
To make scores easy to interpret at a glance, every firm's 0–100 score maps to a letter grade — a standardized report card for AI visibility, on fixed absolute thresholds. It's the same scale as our firm audit report card, and grades are never curved against the #1 firm.
73–100
6 of 300 ranked firms this edition
Consistently recommended
Named across most relevant queries, usually prominently — high positions across nearly every platform.
A+ ≥ 87 (Distinguished) · A ≥ 80 (Excellent) · A− ≥ 73 (Very Strong)
50–72.9
59 of 300 ranked firms this edition
Frequently visible
Reliably visible with solid positioning — a real AI presence, though not always among the first firms named.
B+ ≥ 66 (Strong) · B ≥ 58 (Above Average) · B− ≥ 50 (Solid)
28–49.9
235 of 300 ranked firms this edition
Emerging visibility
Surfaces intermittently. Present in the AI conversation, but not yet a default recommendation.
C+ ≥ 43 (Average) · C ≥ 36 (Building) · C− ≥ 28 (Developing)
5–27.9
below the published top 300
Rarely surfaced
Occasionally named, typically only in narrow or highly specific queries.
D+ ≥ 20 (Limited) · D ≥ 12 (Minimal) · D− ≥ 5 (Very Limited)
0–4.9
below the published top 300
Effectively invisible
Not meaningfully surfaced by AI platforms today — no measurable recommendation presence.
F ≥ 0 (Foundation)
A grade is a snapshot of AI visibility, not a verdict on quality. It reflects how AI assistants surface the firm — not the firm's competence, credentials, or fit for any individual client.
AI assistants tested
We query the four assistants that account for the large majority of US consumer AI usage. Each is tested with the same query set; each contributes to a firm's blended score in proportion to its real-world usage share.
ChatGPT
OpenAI's latest GPT models · with web search
Claude
Anthropic's latest models · with web search
Gemini
Google's latest Gemini models
Perplexity
Citation-first answer engine
Queries run in clean, unpersonalized sessions with web search enabled — no accounts, no conversation history, no location signals beyond the market named in the question. This approximates a neutral first-time user rather than a logged-in profile.
Geographic coverage
We track advisor visibility across 64 major U.S. markets. Location-specific queries (for example, "financial advisor in Austin") run for every covered market, and each market gets its own leaderboard page.
How rankings change
The leaderboard is recomputed every edition. This edition compared to July 2026:
Rank arrows compare like-for-like markets shared with the prior run (64 shared cities plus national queries), so coverage expansion never masquerades as movement — firms first surfaced by new metros carry a "New markets" badge instead of an arrow. And because AI outputs are probabilistic, we measured our own sampling noise: top-25 ranks are cited precisely, while rank changes below the top 25 display as approximate (~) point estimates. Sustained movement across editions reflects real change; single-edition wobble in the mid-board often doesn't.
Methodology changelog
We version the methodology and publish every change — so movement caused by our updates is never confused with movement in the market.
Versioned Change Record
Every methodology change, on the record in plain English — so when a firm's rank moves sharply between editions, the reason is easy to find. Current version: score-v2.0.
Reviewed monthly
Methodology is reviewed every month before publication.
Changed rarely
Changes are batched into scheduled windows and never stacked back to back — every change is followed by at least two stable editions.
Movement means movement
Rank arrows only ever reflect real visibility change — never our own updates, which are disclosed here instead.
The dataset
The research behind this board has been running since November 2025 — nine full sweeps to date, plus dedicated re-runs to measure sampling noise.
November 2025
Research begins — first baseline sweeps of AI advisor recommendations
February 2026
Query library expanded; full-scale sweep
March 2026
Month-over-month rank tracking established
June 2026
31 metros across all four AI platforms
July 2026
Methodology v2 — 0–100 scores, 64 metros, published grade scale
Versioned methodology entries below begin with the current scoring model. Months with no entry had no methodology changes — stability is the default.
- The Washington, D.C. queries now say 'the Washington, D.C. area' — AI platforms were reading the bare name 'Washington' as the state, so earlier D.C. boards included Seattle-area firms
- We audited all 64 markets against SEC-registered headquarters: D.C. was the only market with a name collision
- D.C. board turnover this month is the correction taking effect, not market movement
- A universe audit removed 150 out-of-scope rows — bank wealth arms, insurance agencies, non-US firms, and trust companies without an investment-adviser registration; the board ranks independent US RIAs
- A firm flagged that it wasn't listed despite visible AI presence — we found and fixed an extraction defect that was discarding firm names beginning with “The” (and similar instruction-word shapes); affected firms' mentions are restored in this edition, with the top 25 unchanged
What stayed the same
Scoring methodology, platform weights, coverage, and every other market's queries are untouched.
Next scheduled review: Unchanged: platform usage weights quarterly window, October 2026 edition; no methodology changes in the August or September editions.
Our first published edition — superseded by the current methodology.
Leaderboard metrics explained
Beyond the headline score, each firm's leaderboard entry shows several supporting metrics.
Score (0–100)
The AI Visibility Score — per-platform sub-scores for reach and position, blended by US usage weights. The primary ranking metric.
Visibility grade
The letter grade mapped from the score on fixed absolute thresholds, for quick interpretation.
Change
Rank movement vs July 2026 on like-for-like markets. "New markets" badge = entered via coverage expansion; ~ = approximate (outside the top 25).
Queries with mention
How many of the 6,420 queries surfaced the firm at least once — raw breadth regardless of position.
Platform sub-scores
A 0–100 score per assistant, with reach %, average position, and query counts in the expanded row — shows exactly where a firm is strong or invisible.
Summary
A plain-language read of each firm's standing, drivers, and biggest platform gap, in the expanded row.
City-specific leaderboards
Alongside the national view, every covered market has its own board ranking the top 15 firms AI assistants name for that metro — each verified as a registered investment adviser against the SEC/state Form ADV registry, so every listed name is a real firm. Because most people ask for advisors "near me," local visibility is often where firms compete most directly. Firms below a city's top 15 remain tracked and searchable on the boards.
- Local queries — each market is scored with location-specific questions a real prospect would ask there.
- Local field — a firm is ranked only against others surfaced for the same market, not the whole country.
- Local movement — rank changes are tracked per market, so a firm can lead in one city while emerging in another.
A firm's national rank and its rank in a specific city can differ substantially — city standings are measured from that city's queries alone.
Understanding your score
If you run or market an RIA, your score is a diagnostic — it tells you whether AI assistants know your firm exists and surface it when it matters. In our analysis, visible firms tend to share four traits:
Discoverability
Your firm's information exists in the sources assistants read — your site, directories, and authoritative third parties.
Content clarity
Specialties, locations, and services stated clearly enough for an assistant to match you to a query.
Credible citations
Reputable sources reference your firm — media, rankings, and professional directories assistants treat as signals of trust.
Machine-readable structure
Site markup and consistent location data that assistants can parse without guessing.
Improving your visibility
This is not traditional SEO — keyword stuffing, backlink schemes, and paid placements have minimal impact. AI visibility responds to things you control:
Make your firm legible
State clearly who you serve, where, and how you're compensated. Ambiguity is the most common reason a firm is skipped.
Add structured data
Use schema markup for your organization, services, and locations so assistants can parse your details without guessing.
Earn credible citations
Get referenced by directories, publications, and associations assistants already trust.
Publish genuinely useful content
Answer the real questions prospects ask. Assistants favor sources that directly address the query.
Monitor and iterate
Re-check your visibility each edition and adjust. This is an ongoing practice, not a one-time fix.
Limitations & transparency
We hold ourselves to the same scrutiny we apply to the data. Here's exactly what this leaderboard can and cannot tell you.
What it can tell you
- How often a firm is surfaced by the four major AI assistants.
- How a firm's visibility compares to peers, nationally and per market.
- Which assistants do and don't recommend a firm — and how prominently.
- How visibility trends across editions, on a comparable 0–100 scale.
What it cannot tell you
- Whether a firm is a good fit for any individual client.
- A firm's investment performance or service quality.
- What any specific user sees — live answers vary and can be personalized.
- Anything about visibility outside our covered markets and query set.
Methodology FAQ
The detail behind the most common questions about how the data is produced.
Update frequency
Scores & ranks
Recomputed monthly from a fresh run of the full 6,420-query library, published within days of completion.
Firm universe
Rebuilt each edition from observed AI mentions, with entity filters and canonical clustering applied before scoring.
Methodology
Versioned and published in the changelog above. Changes are batched into scheduled windows, never stacked.
Independent research notice
This is independent research, not investment advice or an endorsement. AdvisorFinder is not affiliated with, sponsored by, or endorsed by OpenAI, Anthropic, Google, or Perplexity. Assistant names and logos identify the tools we test and are the property of their respective owners.
Firms cannot pay for improved rankings. All data is collected through standardized queries, and nothing on this page is a recommendation to engage or avoid any firm.
Contact & corrections
Spotted something that looks wrong? We review every correction request. If your firm's factual details are misrepresented, or you have questions about the methodology, get in touch:
intelligence@AdvisorFinder.com
AI Visibility Report
See exactly how AI presents your firm
Order an analyst-written AI Visibility Report showing where your firm appears, how AI describes it, and the highest-priority gaps to close.
Secure payment via Stripe · report delivered within one business day
What's in your report
- Your visibility across ChatGPT, Gemini, Claude and Perplexity
- See how each AI chatbot describes your firm
- Competitive gap analysis against firms in your market
- Six prioritized fixes, split into quick wins and strategic moves
- 10-page PDF report, delivered by email
Last updated: August 12, 2026
Methodology version: score-v2.0