Independent AI Visibility Score audit of Dharmesh Shah using the canonical IdeaLab AI Visibility OS v1.1 framework. Composite: 90/100 · Grade A — Answer Authority. Dharmesh Shah is co-founder and CTO of HubSpot ($HUBS), publisher of the simple.ai newsletter (2M+ subscribers), founder of agent.ai, author of OnStartups.com (700K+ members), co-author of "Inbound Marketing" (Wiley), and angel investor in 60+ startups. This audit places him #2 in the IdeaLab influencer database. Framework by J.L. Marcoux, IdeaLab.ai.
🏆
Key Finding: Grade A · #2 in IdeaLab Database · Highest D7 Trust Signal Score
Dharmesh Shah scores 90/100 — second only to Neil Patel (91). D7 Trust Signals (93/100) is the highest score in the entire IdeaLab database: SEC-filed public company CTO, MIT + UAB credentials, Wikipedia page, Wiley-published book, Inc. Founders 40, and 2M+ newsletter subscribers. The only meaningful gap is entity fragmentation — brand operating across 4 active domains with no single canonical personal entity hub. Estimated fix: +2–4 points to reach 92–94/100.
All dimensions Grade A or B. Highly uniform — radar nearly fills the chart.
DIMENSION SCORES
D1
8810%
D2
9120%
D3
8615%
D4
9220%
D5
8915%
D6
9110%
D7
93 ★10%
PROJECTED POST-FIX
92–94 / 100
After entity consolidation · Person JSON-LD
WEIGHTED SCORECARD
Dimension
Weight
Score
Weighted
Grade
D1Brand Clarity
10%
88
8.80
B
D2Content Depth
20%
91
18.20
A
D3Entity Recognition
15%
86
12.90
A
D4Citation Network STRONGEST
20%
92
18.40
A
D5Topical Authority
15%
89
13.35
A
D6Recency & Freshness
10%
91
9.10
A
D7Trust Signals ★ DATABASE RECORD
10%
93
9.30
A
TOTAL
100%
—
90
A
DIMENSION ANALYSIS — Click to expand
DATABASE BENCHMARK
NP-001Neil Patel
91
NP-NEWDharmesh Shah ◀ THIS AUDIT
90
NP-003Rand Fishkin
87
NP-008Seth Godin
84
NP-005Jay Baer
81
NP-006Marcus Sheridan
81
NP-002Gary Vaynerchuk
79
NP-004Ann Handley
77
NP-007Justin Welsh
75
* Dharmesh Shah (90) trails Neil Patel (91) by 1 point. Gap is entirely attributable to entity fragmentation across 4 domains and the HubSpot-anchoring of citation equity. D7 Trust Signals (93) is the highest score in the database. D4 Citation Network (92) is tied for the highest in the database. Grade A on all 7 dimensions.
Every IdeaLab audit uses the AI Visibility OS v1.1 — a seven-dimension framework that measures how discoverable, citable, and trustworthy an entity is to answer engines like ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Each dimension is scored 0–100, multiplied by its weight, and summed into a composite 0–100 score. The composite maps to a letter grade that describes AI-answer behavior for the entity's core queries.
D1 · BRAND CLARITY
WEIGHT ×1.0 · 10%
Consistent, unambiguous naming and positioning across owned surfaces. AI engines cite entities that resolve to a single canonical identity.
D2 · CONTENT DEPTH
WEIGHT ×2.0 · 20%
Long-form, first-hand, evidence-rich content on owned domains. Depth is the largest single driver of LLM citation frequency.
D3 · ENTITY RECOGNITION
WEIGHT ×1.5 · 15%
Presence in knowledge graphs — Wikipedia article, Wikidata QID, Google Knowledge Panel, sameAs coverage. Anchors the entity for AI retrieval.
The two heaviest weights — D2 Content Depth and D4/D6 Structured Knowledge and Citations — reflect the empirical drivers of AI answer inclusion. D3 Entity Recognition acts as a ceiling: without Wikipedia and Wikidata anchoring, most audits cap at Grade B regardless of the other six dimensions.
GRADE SCALE
A · 85–100
Answer Authority — AI engines cite the entity by default
B · 70–84
Citation Ready / Strong — cited when prompted specifically
C · 60–69
Moderate / Legacy Mode — inconsistent AI visibility
D · 50–59
At Risk — rarely surfaced without exact-name prompts
F · <50
Invisible — no reliable citation surface for AI answers
The per-dimension scores and weighted contributions for this specific audit are shown in the scoring table above. The Top Gap and Top Action highlighted on this page correspond to the lowest-scoring high-weight dimensions — the fixes with the largest projected uplift to the composite score.
Score Methodology FAQ
Frequently Asked Questions About The AI Visibility Score
What is the AI Visibility Score?
The AI Visibility Score is a 0–100 composite metric produced by IdeaLab's AI Visibility OS v1.1 framework. It measures how discoverable, citable, and trustworthy an entity — a brand, creator, product, or expert — is to generative answer engines such as ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Higher scores correlate with more frequent and more confident AI citations for the entity's core queries.
How is the score calculated?
Each of the seven dimensions (D1–D7) is scored 0–100, multiplied by its weight, and summed. The formula is: Score = (D1 × 0.10) + (D2 × 0.20) + (D3 × 0.15) + (D4 × 0.20) + (D5 × 0.15) + (D6 × 0.20) + (D7 × 0.10). Content Depth (D2), Structured Knowledge (D4), and Social Proof & Citations (D6) carry the largest weights because they are the empirical drivers of AI citation behavior.
What do the letter grades A through F mean?
Grade A (85–100) is Answer Authority — AI engines cite the entity by default. Grade B (70–84) is Citation Ready — cited when prompted specifically. Grade C (60–69) is Moderate / Legacy Mode with inconsistent AI visibility. Grade D (50–59) is At Risk — rarely surfaced without exact-name prompts. Grade F (below 50) is Invisible — no reliable citation surface for AI answers.
Why does Entity Recognition (D3) act as a ceiling?
Without a Wikipedia article, a Wikidata QID, and consistent sameAs coverage across owned surfaces, AI engines cannot reliably resolve the entity to a single canonical identity. In practice this caps most audits at Grade B regardless of how strong the other six dimensions are, because the retrieval layer that grounds AI answers depends on knowledge-graph anchoring.
What is the difference between the Top Gap and the Top Action?
The Top Gap is the single lowest-scoring high-weight dimension for the audited entity — the diagnosis. The Top Action is the concrete, prioritized fix that addresses that gap and delivers the largest projected uplift to the composite score. Together they turn a static score into an execution roadmap.
How often is an AI Visibility Score refreshed?
IdeaLab audits are refreshed when the entity ships meaningful changes — a Wikipedia article goes live, new JSON-LD schema is deployed, an llms.txt is published, or major press citations land — and on a rolling quarterly cadence to capture drift in AI-crawler policy, knowledge-graph coverage, and citation surface.
Related audits
Closest matches by category, keyword overlap, and AI Visibility Score — all scored on the same 7-dimension framework.