IDEALAB · AI VISIBILITY OS v1.1BRAND AUDIT · BR-001FIRST BRAND IN DATABASEGRADE B · CITATION READYD5 TOPICAL AUTHORITY 82 · CATEGORY LEADER$200M+ REVENUE · ROBB WOLF CO-FOUNDER
AI Visibility Score Report
drinklmnt.com · science.drinklmnt.com·
LMNT — Zero Sugar Electrolytes · Founded 2018 · Robb Wolf Co-Founder · Peter Attia Investor · Huberman Lab Partner·June 25, 2026
Independent AI Visibility Score audit of LMNT (Drink LMNT, Inc.) using the IdeaLab AI Visibility OS v1.1 framework. Composite: 70/100 · Grade B — Citation Ready · BR-001 · First brand audit in IdeaLab database. LMNT is a zero sugar electrolyte drink mix brand founded in 2018 by Robb Wolf (NYT bestselling author, former research biochemist). Revenue $72M DTC (2025), $200M+ all-channel (2023). Partners: Andrew Huberman (Stanford, Huberman Lab), Peter Attia (The Drive, investor), USA Weightlifting. Framework by J.L. Marcoux, IdeaLab.ai.
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Key Finding: First Brand Audit — D5 Topical Authority (82) Drives the Score
LMNT scores 70/100 — Grade B, Citation Ready — entering the IdeaLab database exactly at the Grade B threshold. The Huberman Lab, Peter Attia, and Modern Wisdom partnerships function as authority-based distribution: when Huberman or Attia discusses electrolytes, AI engines associate LMNT with scientific electrolyte authority. D5 Topical Authority (82/100) is the highest dimension — "zero sugar electrolytes" is a query where LMNT is the category-defining answer. D3 Entity Recognition (48/100) is the critical gap — no Wikipedia, no Wikidata, no Organization JSON-LD despite $200M+ in revenue.
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Critical Gap: D3 Entity Recognition — 48/100 · Grade C
No Wikipedia brand page. No Wikidata entry. No Organization JSON-LD on drinklmnt.com. LMNT has a Stanford neuroscientist as sponsor, an NYT #1 bestselling author as investor, and an NYT bestselling author as co-founder — yet AI knowledge graphs cannot confidently resolve "LMNT" as a brand entity. Deploying Organization JSON-LD + creating a Wikipedia article + creating a Wikidata entry can add +18-22 points to D3 alone.
⬡ = Brand audit ○ = Influencer/Person audit. LMNT (70/100 BR-001) is the first brand in the IdeaLab database. It enters at Grade B floor level, ahead of Amanda Natividad (60/100). LMNT's D5 Topical Authority (82) is the highest-scoring D5 of any non-Grade-A entity in the database — matching the D5 scores of Jay Baer (91) and Marcus Sheridan (91) who are the D5 leaders. Projected post-fix: 78–82/100, placing LMNT well into Grade B mid-range, ahead of Brian Dean (74) and Justin Welsh (75).
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.
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