Why Your Company Is Invisible to AI (And What That’s Already Costing You)
Last updated: June 26, 2026
Maria Dykstra is an AI Visibility Architect who has diagnosed algorithmic authority failures for 50+ B2B companies. She built global ad systems at Microsoft that drove $2B in revenue across 1B+ ads per month, ran TreDigital for 13 years across Fortune 500s and startups, and works with agentic AI companies translating infrastructure into go-to-market strategy.
TL;DR
Most B2B companies are invisible to the AI systems now shaping buyer shortlists. Not because they lack expertise. Because their digital presence is structurally illegible to machines. Fewer than 10% of top Google results appear in AI-generated answers. Two-thirds of B2B buyers now use generative AI as much as or more than traditional search. The gap between human visibility and machine visibility is widening every quarter.
We tested this across 20 B2B companies in five industries and three AI platforms. The results: a $2.5B company outscored a $400B company. A company with 2,250 employees outscored one with 12,000. Market leadership did not predict AI authority. Structure did.
The Algorithmic Authority Stack is a 7-layer diagnostic framework that identifies exactly where companies break. This post introduces the framework, the evidence, and a 5-minute self-diagnostic you can run right now.
Table of Contents
Ask OpenAI’s ChatGPT: “Who are the top 5 companies in [your space]?”
If you’re not listed and you should be, you don’t have a marketing problem. You have an algorithmic authority problem. Your competitors aren’t better. They’re structurally legible to AI systems. You’re not.
After building ad systems that served 1B+ ads/month at Microsoft, I spent 13 years running TreDigital and the last two years auditing B2B companies for AI visibility. The failure pattern is always the same. Companies with real expertise, real market share, real reputation. Invisible to the systems now deciding who gets recommended.
In Wave 1 of The Algorithmic Authority Index, we ran a structured test across 20 B2B companies in SaaS, Fintech, Healthcare, Manufacturing, and Professional Services. Four competitive tiers. Three AI platforms. 420 individual layer assessments.
The finding that should make you stop:
Teladoc Health ($2.5B revenue) outscored UnitedHealth Group ($400B revenue). Mercury (200 employees) outscored Brex (1,000+ employees). Wheel Health (a T4 startup) outscored Hims & Hers (NYSE-listed). In every industry, smaller companies with clean structural positioning outperformed larger competitors with messier signals.
Market leadership did not predict AI authority. Revenue did not predict it. Headcount did not predict it. What predicted it: structural legibility. The companies AI could read, classify, and cite in one pass. Those companies won. Regardless of size.
Your forty years of expertise is a human signal. The algorithm never sees it.
This is what The Invisible Audience looks like. You built authority for one system. The decision-making moved to another.
Why are companies invisible to AI?
Human visibility and machine visibility have decoupled. Completely. Your brand, your reputation, your relationships — none of it transfers.
You built recognition over decades. Trade shows. Referrals. Board seats. Speaking engagements. OpenAI’s ChatGPT doesn’t attend your conferences. Perplexity AI doesn’t take referrals. Google’s Gemini doesn’t care about your executive relationships.
These systems build their understanding of your company from one thing: your digital structure. Not your digital presence. Your digital structure. Presence means you exist online. Structure means AI can parse what you are, classify it, and retrieve it when a buyer asks.
Gartner predicted traditional search volume would drop 25% by 2026 as AI chatbots replace queries (source). That prediction is playing out. Responsive’s 2025 Inside the Buyer’s Mind report found two-thirds of B2B buyers now use generative AI as much as or more than traditional search when researching vendors. In tech, that number hits 80%.
The 6sense 2025 Buyer Experience Report found that 95% of the time, buyers choose a vendor already on their Day One shortlist. They fill that shortlist before they talk to a single salesperson. If AI is shaping that shortlist and you’re structurally invisible, the evaluation never starts.
You don’t lose the deal. You never knew it existed.
How do AI answers actually get built?
Not the way Google results get built. Google works like a librarian — it indexes pages, scores them, and ranks them. The ranking persists. AI answer generation works like a researcher assembling a briefing from scratch. Every time. There is no “page one.” No persistent ranking. No saved list.
| Traditional SEO (Google) | AI Retrieval (ChatGPT, Perplexity, Gemini) | |
| How it works | Pages ranked in a persistent index | Answer rebuilt from scratch every query |
| What you optimize | Keywords + backlinks | Entity clarity + signal consistency |
| What decides inclusion | Domain authority + engagement | Entity resolution + cross-source corroboration |
| How long it lasts | Ranking persists until displaced | No persistence. Every query is fresh. |
| Study evidence | Salesforce ranks page 1 for hundreds of CRM queries | L4 (citation layer) averaged 1.6/3.0 across all 20 companies. Even Salesforce: 1.7. |
Here’s what happens when a buyer asks AI to recommend companies in your category. In our testing across ChatGPT, Perplexity, and Gemini, the pipeline followed four phases:
Step 1: Query decomposition. The system breaks the question into semantic components. “Environmental monitoring” becomes a category. “Companies” signals entity type. “Top” signals the user wants a ranking.
Step 2: Entity retrieval. The system searches for entities that match. This is not keyword matching. It’s entity resolution. Can the system verify this company from multiple independent sources? Does it resolve cleanly to one identity? This is where your company breaks or survives. If you use six different descriptions across six surfaces, the system retrieves six fragments. It can’t merge them. Your competitor with one consistent description resolves instantly. They enter the candidate pool. You don’t.
Step 3: Signal weighting. For each entity, the system evaluates signal strength. Independent source mentions. Expert classification. Semantic consistency. Structured data corroboration. Each confirming signal adds weight. Each missing signal subtracts it.
Step 4: Answer assembly. The system ranks entities by signal weight and assembles a natural-language answer. Strong signals get named. Weak signals get excluded. Not penalized. Excluded. The system doesn’t say “this company is bad.” It says “I don’t have enough clean signal to include this company.” Then it moves on.
This runs in milliseconds. No human reviews it. The system can’t classify you fast enough to include you. So it doesn’t.
Why do ChatGPT, Perplexity, and Gemini see your company differently?
You have one company. AI has three opinions about it. We scored every layer of the Algorithmic Authority Stack separately for each platform across all 20 companies — 420 individual assessments. The three platforms agreed only 69% of the time. In 44 out of 140 layer assessments, at least one platform scored a company differently from the others.
The largest divergence: UnitedHealth Group. ChatGPT gives it a score of 100. Perplexity gives it 81. A 19-point spread for the largest healthcare company on earth.
Your prospect’s platform choice determines whether you look authoritative or vulnerable.
ChatGPT is The Advisor. Average score: 2.60/3.00 per layer. Highest of the three. It cites sources with URLs. It captures leadership changes within months. It found UHG’s CEO departure (May 2025) before Gemini knew it happened. ChatGPT scored highest on the citation layer (L4) in 19 of 20 companies. It also surfaces negative trust signals: DOJ investigations, FTC probes, cyberattack reports sourced to JAMA. ChatGPT doesn’t just confirm trust. It interrogates it.
Perplexity is The Auditor. Average score: 2.42/3.00. Lowest of the three. It’s the only platform that regularly cites your actual URL. Vanta gets Perplexity citations for SOC 2 content. Wheel Health gets them for virtual care infrastructure. When your content is structured for extraction, Perplexity finds it. When it’s not, Perplexity falls back on whatever promotional blog ranks highest. The quality is bimodal: either your URL or a random third-party blog.
Gemini is The Recommender. Average score: 2.49/3.00. It produces the strongest authority language. “Category creator” for Teladoc. “Gold standard” for Stripe. “No other company has successfully integrated insurance and clinical care at this scale” for UHG. Gemini doesn’t hedge. But it has the most outdated data for smaller companies and rarely cites specific sources.
The diagnostic implication: Platform convergence indicates structural legibility. Platform divergence indicates signal fragmentation. Salesforce, Stripe, and Deloitte show tight convergence across all three. Rho, Slalom, and Hims & Hers show wide divergence. The divergence itself is the diagnostic. Read more about what each platform sees →
What does “structurally legible” mean?
Structural legibility means AI can read your company and classify it in one pass. If it can’t, it doesn’t guess. It excludes. Three things have to happen in sequence, in milliseconds, or you’re out.
Entity resolution. Can the system identify your company as a single, distinct entity? This requires consistent naming across every surface. Homepage. LinkedIn. Google Business Profile. Partner listings. If your homepage says “Data Solutions Platform,” your LinkedIn says “Environmental Monitoring Provider,” and your about page says “Compliance Consulting Firm,” the system retrieves three fragments. It can’t merge them. I tested this across 15 Series B companies in Q4 2025. Twelve had contradictory identity signals across 5+ surfaces. All twelve were invisible to ChatGPT.
Category classification. Once resolved, can the system assign you to one category? If you describe yourself as a “platform,” a “solutions provider,” a “partner,” and a “tool” across different surfaces, the system faces a four-way conflict. It doesn’t pick the most likely one. It drops you from the retrieval pool for all four.
Signal corroboration. Does external evidence confirm the classification? The system cross-references your self-description against third-party sources — review platforms, Wikipedia, industry directories. If no external source classifies you the way you classify yourself, the system treats your claim as unverified. Unverified entities rank below verified ones. Every time.
This is Layer 1 of the Algorithmic Authority Stack: Market Identity Clarity . In our 20-company study, all 20 companies scored perfectly on Layer 1. AI knows what every company does.
That’s the uncomfortable part. AI knows what you do. It doesn’t know why you matter. The separation happens at Layer 2 (does AI treat you as an authority or a participant?), Layer 4 (does AI cite your content?), and Layer 6 (how AI models read E-E-A-T signals and verify your trustworthiness?). Those are the layers where companies break.
“Our website says ‘platform,’ our LinkedIn says ‘solutions provider,’ and our sales deck says ‘partner.’ I never noticed until you pointed it out.” — VP Marketing, B2B fintech
Across the 20-company Wave 1 study, semantic inconsistency was the most common structural break below the citation layer. Run the identity fragmentation test on your company →
How does AI decide who to cite?
AI doesn’t cite the company that writes the most about a topic. It cites the entity with the most consistent, verifiable, cross-source signal. Those are not the same thing. Most companies assume they are. That assumption is why the citation layer (L4) averaged 1.6/3.0 across all 20 companies in our study.
Fewer than 10% of top Google results appear in AI-generated answers (Semrush, 2024). Ahrefs found only 12% overlap between AI-cited sources and Google’s top 10 results. Google rankings and AI citations are not the same system.
Most companies get this catastrophically wrong. They spend $15K/month on SEO. They rank on page one. They assume AI will find them too. It won’t.
The Deloitte Paradox. Deloitte publishes more thought leadership than almost any organization on earth. Hundreds of reports annually. In our study, Deloitte scored L4=1.7. Zero AI platforms cited deloitte.com as the domain authority for management consulting. When you ask ChatGPT about consulting best practices, it cites PMI, ISACA, Gartner, and CIO.com. Not the firm that has done more implementations than anyone.
This isn’t unique to Deloitte. L4 is the weakest layer in the entire framework. No company in any industry has consistently solved it. Read more about training-ready content and fixing citation invisibility→
Why? Because AI doesn’t cite the vendor. It cites the institution with the most neutral, standards-aligned authority. The only exception in our study: ChatGPT scored L4=3 for UnitedHealth Group. Healthcare has peer-reviewed journals (PMC), government guidelines (CMS Innovation Center), and improvement science organizations (IHI). That evidence infrastructure doesn’t exist in SaaS, Fintech, Manufacturing, or Professional Services. Read our study on why high-trust industries face the hardest AI visibility problem →
The inputs that actually drive AI citation:
Entity clarity. Can AI identify who wrote this, who published it, and what organization stands behind it? SE Ranking’s 2024 analysis found pages with expert quotes averaged 4.1 AI citations versus 2.4 for pages without. Princeton’s GEO research found expert attribution improved AI visibility by 30-40%. Named experts are parseable. Anonymous content is noise. Read more about expertise architecture →
Semantic consistency. Does your content use the same terminology across pages, or does it drift? What I call Semantic Drift — using multiple different terms for the same concept — is the most common classification failure I find. Your homepage says “AI-powered compliance platform.” Your LinkedIn says “enterprise validation partner.” Your product page says “GxP automation tool.” Four surfaces, four category signals. AI processes four separate classifications instead of one reinforcing signal. In 50+ audits, Semantic Drift appeared as the primary classification failure in 80% of companies.
Structural density. Does the content answer specific questions with specific data? Replace “significant growth” with “27% increase in Q3 2025.” Replace “leading provider” with “serving 200+ pharmaceutical facilities across 14 countries.” Every vague adjective is a signal AI can’t index.
Cross-source corroboration. AI builds recommendation lists from how often you’re mentioned across the content it trusts. YouTube (~23.3%), Wikipedia (~18.4%), and Google.com (~16.4%) dominate AI citation sources according to Surfer’s 2025 AI Citation Report. Your website is one signal. The ecosystem is the system. Read more about touchpoint presence → ]
| Source | Finding |
|---|---|
| Semrush (2024) | Fewer than 10% of top Google results appear in AI-generated answers |
| Ahrefs (2024) | 12% overlap between AI-cited sources and Google top 10 |
| SE Ranking (2024) | AI-cited content averages 2,900 words; expert quotes increase citations from 2.4 to 4.1 |
| Princeton GEO (2024) | Expert attribution improves AI visibility by 30-40% |
| 6sense (2025) | 95% of buyers choose from Day One shortlist |
| Surfer (2025) | YouTube (23.3%), Wikipedia (18.4%), Google (16.4%) dominate AI citation sources |
For every piece of content AI cites, it processes roughly 43,000 it doesn’t . The filtering is structural, not editorial. The algorithm doesn’t read your content and judge whether it’s good. It determines whether it’s classifiable. If it’s not classifiable in milliseconds, it’s excluded.
What’s the Algorithmic Authority Stack?
Seven structural layers. Each independently testable. Each breaks independently. The breaks compound.
I built the Algorithmic Authority Stack framework after the pattern became undeniable. Every audit, same failures. Different companies, different industries, different sizes. After 50+ audits, I stopped finding exceptions. Then we validated it with structured data: 20 companies, five industries, three platforms, 420 assessments. The framework held.
Layer 1: Market Identity Clarity. Can AI resolve your company as one entity in one category? In our study, all 20 companies scored perfectly. This is table stakes. If you fail here, nothing else matters. But passing doesn’t make you visible. It makes you eligible. Read more about Layer 1 →
Layer 2: Expertise Architecture. Does AI treat you as an authority or a participant? This is where the separation begins. Gemini calls Salesforce “pioneer of cloud-based CRM.” It calls monday.com “popular project management and work OS platform.” Pioneer vs popular. Authority vs feature language. Every company that scored 94 in our study was identified by AI as having invented their category. Salesforce. Stripe. Teladoc. The question isn’t “are you the biggest?” It’s “did you invent the category?” Read more about Layer 2 →
Layer 3: Semantic Density. Does your content speak in terms AI can index? One fact per sentence. Named entities, not vague references. Specific numbers, not adjectives. Consistent terminology, not synonym rotation. I’ve watched companies use twelve different terms for the same offering. AI processes twelve different topic signals. Not one company. Read more about Layer 3 →
Layer 4: Training-Ready Content. Is your content structured for AI retrieval? This is the universal bottleneck. L4 averaged 1.6/3.0 across our entire study. SE Ranking found the average word count of AI-cited content is 2,900 words. The Content Marketing Institute’s 2025 report shows most B2B companies publish posts averaging 300-500 words. Three well-structured posts outperform twelve unstructured ones. Every time. Read more about Layer 4 →
Layer 5: Algorithmic Touchpoint Presence. Do you appear where AI actually looks? AI cross-references you across review platforms, Wikipedia, Reddit as a Trust Seed for AI citations, trade publications, industry directories, and comparison content. SE Ranking found domains with over 10M Reddit mentions averaged 7 ChatGPT citations. Domains with minimal activity averaged 1.8. You can’t win on one surface when AI scores across dozens. Read more about Layer 5 →
Layer 6: Trust & Proof Signals. Does AI have evidence that others trust you? In our study, Wheel Health (a T4 startup with HITRUST + SOC 2 Type II + HIPAA certifications) outscored Hims & Hers (NYSE-listed, $3.6B market cap). Certifications are more parseable than prestige. AI reads structured trust signals. Not client logos. Read more about Layer 6 →
Layer 7: Visibility Measurement. Can you even measure whether AI finds you? Google Analytics tells you who visited your site. It tells you nothing about whether AI recommended you in the first place. Read more about Layer 7 →
“We track rankings, traffic, leads. Nobody has ever measured whether AI can find us. We didn’t even know that was a metric.” — CMO, mid-market B2B
Does company size determine AI authority?
No. The data is unambiguous.
We called it the Tier Paradox. It appeared in industry one and held through industry five. In every industry we tested, at least one smaller company outperformed a larger competitor. See why competitors show up and you don’t →
| Industry | Higher Scorer | Score | Lower Scorer | Score | Gap |
| Healthcare | Teladoc (T2) | 94 | UHG (T1) | 92 | $2.5B > $400B |
| Fintech | Mercury (T3) | 86 | Brex (T2) | 81 | T3 > T2 |
| SaaS | Vanta (T3) | 84 | monday.com (T2) | 83 | T3 > T2 |
| Pro Services | West Monroe (T4) | 79 | Slalom (T3) | 74 | T4 > T3 |
| Healthcare | Wheel Health (T4) | 81 | Hims & Hers (T3) | 80 | T4 > T3 |
The pattern: companies that own a narrow, non-derivative category outperform companies that compete head-to-head with larger players. Mercury owns “banking for startups.” Brex competes with Amex for “corporate cards.” When the question is “best bank for startups,” Mercury is the answer. When the question is “best corporate card,” Brex is one of several options.
The most extreme case: Tally. Scored 57. Lowest in the study. Not because of bad content. Because ChatGPT describes Tally as a “Typeform alternative.” One word. Alternative. That word means derivative. Derivative means deprioritized. Your identity belongs to the company you’re defined against.
AI does not measure revenue. It measures semantic primacy. The company that created the category is the default answer to every question about that category.
What does AI invisibility cost in real pipeline dollars?
Conservatively: 20-40% of your pipeline is invisible to you.
Responsive’s 2025 research found 47% of B2B buyers use AI for vendor discovery. Among tech buyers, 56% cite chatbots as a top source. Forrester’s 2026 State of Business Buying report confirms generative AI tools were the single most cited interaction type for researching purchases.
If 47% of buyers use AI for vendor discovery and your company is excluded from retrieval, up to half of your total addressable demand never enters your CRM. That affects pipeline, CAC, revenue predictability, how your board evaluates category leadership, and valuation.
The 6sense data shows 95% of the time, the winning vendor was already on the Day One list. If AI shapes that list and you’re not on it, the evaluation never starts. You’re not losing to competitors in head-to-head evaluations. You’re being filtered out before the evaluation begins.
“I asked ChatGPT for the top 5 companies in our space. We weren’t listed. A company with half our revenue and a third of our team was.” — Series B SaaS founder
Thirty days after restructuring Layers 1-4, they went from zero Perplexity AI citations to eleven. No rebrand. No redesign. No new content. They restructured what they already had. Read the full step-by-step guide on how to get cited by Perplexity →
“We publish 12 blog posts a month. Our competitor publishes 3. They show up in AI answers. We don’t.” — Content Director, enterprise SaaS
In our study, Deloitte — one of the highest-volume content publishers in B2B — was not cited for their own domain expertise. Volume without structure produces what I call the Volume Paradox: each new post without strong entity signals adds conflicting classification data rather than reinforcing it. Three posts with clean structure outperform twelve without it. Why content volume makes you less visible →
How do you test your own AI visibility?
Five minutes. Five tests. Do them right now.
Test 1: The Category Test. Open ChatGPT. Ask: “Who are the top 5 companies in [your exact category]?” Don’t use your company name. If you’re not listed, you have an algorithmic authority problem. This tests Layer 1.
Test 2: The Cross-Platform Test. Run the same query on Perplexity and Gemini. Compare. In our study, platforms agreed only 69% of the time. If you appear on one but not another, the problem is distribution (Layer 5). If you’re missing from all three, the problem is structural (Layers 1-3).
Test 3: The Identity Fragmentation Test. Open your website, LinkedIn, and pitch deck. Count how many different ways you describe what your company does. More than two means Identity Fragmentation. In the average company I audit, the number is between three and five. The worst I’ve seen: nine. Run the full identity fragmentation test →
Test 4: The Semantic Drift Test. Search your own website for your core offering. Count the synonym variants. “Platform.” “Solution.” “Tool.” “System.” “Suite.” More than two terms used interchangeably means Semantic Drift. That drift compounds across every surface AI reads.
Test 5: The Citation Test. Google “[your company] vs [competitor].” If no third-party comparison content exists, AI has nothing to reference when a buyer asks how you compare. You’re absent from the conversation that shapes the shortlist.
“You showed me that Perplexity recommended three of our competitors by name and described us as ‘and others in the space.’ That’s when I knew.” — CEO, B2B professional services
| Test | What to Do | What It Reveals | Layer |
| 1. Category | Ask ChatGPT: “Top 5 in [your category]” | Whether AI retrieves your entity | Layer 1 |
| 2. Cross-Platform | Run same query on Perplexity + Gemini | Structural vs distribution problem | Layer 5 |
| 3. Identity | Count descriptions across website, LinkedIn, deck | Whether AI has competing entity descriptions | Layer 1 |
| 4. Semantic Drift | Count synonym variants on your site | Whether terminology fragments your signal | Layer 3 |
| 5. Citation | Google “[you] vs [competitor]” | Whether third-party corroboration exists | Layer 6 |
Scoring: Fail tests 1 and 2? AI can’t retrieve your entity. Start with Layers 1-3. Pass test 1 but fail test 2? Distribution problem (Layer 5). Fail tests 3 and 4? Identity Fragmentation and Semantic Drift are your primary breaks. Most companies break in Layers 1 and 3 first. Fix those and the other layers start producing returns. Run the full 5-minute diagnostic →
→ Take the full Algorithmic Authority Audit across all 7 layers.
Do I really need to focus on AI visibility now?
Yes. And the cost of waiting compounds every quarter. AI systems are building their models of your industry right now. The training data that shapes next quarter’s recommendations is being ingested today. If your digital presence is structurally illegible when those models process it, you’re not just invisible now. You’re being trained out.
Forrester’s 2025 Buyers’ Journey Survey found generative AI tools were the single most cited interaction type for purchase research. 6sense shows shortlists form before sellers are contacted. Princeton’s GEO research proves structural optimization improves AI visibility by 30-40%.
In our study, the industry gradients tell the story:
| Industry | T1 | T2 | T3 | T4 | Range | Shape |
| SaaS | 94 | 83 | 84 | 57 | 37 pts | Steep T4 cliff |
| Fintech | 94 | 81 | 86 | 74 | 20 pts | Gradual |
| Manufacturing | 92 | 92 | 83 | 76 | 16 pts | T1 = T2 |
| Pro Services | 90 | 92 | 74 | 79 | 18 pts | T2>T1, T4>T3 |
| Healthcare | 92 | 94 | 80 | 81 | 14 pts | Tightest |
SaaS has a 37-point cliff between T1 and T4. Healthcare has only 14 points. The gradient shape tells you how much structural work separates you from the leader. In manufacturing, the gap is closable. In SaaS, it requires fundamental repositioning.
Inside the agentic AI company where I’m currently embedded, I’m watching what comes next. AI systems that don’t just recommend vendors. AI agents that select them. Negotiate. Purchase. If you’re invisible to today’s recommendation engines, you won’t exist in tomorrow’s agentic ones.
The window to build structural legibility isn’t closing. It’s narrowing. And what’s on the other side of it is permanent.
Most companies start with the Snapshot.
48-hour diagnosis. Tells you exactly which layer is broken so you don’t fix the wrong one first.
Frequently Asked Questions
What is algorithmic authority?
Algorithmic authority is a company’s structural legibility to AI systems. It measures whether AI can resolve your company as a named entity, classify it into a specific category, and retrieve it in response to buyer queries. Algorithmic authority is different from brand authority or domain authority. A company can have strong brand recognition among humans and zero algorithmic authority with AI systems. The Algorithmic Authority Stack is a 7-layer diagnostic framework that tests each structural component independently.
Why doesn’t my Google ranking help me show up in AI answers?
Because Google rankings and AI citations are not the same system. Semrush’s research found that AI Overviews and organic top 10 results have minimal URL overlap. Ahrefs found only 12% of AI-cited URLs also rank in Google’s top 10 for the same query. Google ranks pages based on backlinks, click-through rates, and keyword relevance. AI systems cite content based on entity clarity, semantic consistency, structural density, and citation frequency across trusted third-party sources.
How do I know if my company is invisible to AI?
Run the ChatGPT Test. Open OpenAI’s ChatGPT and ask: “Who are the top 5 companies in [your exact category]?” Do not include your company name in the query. If you’re not listed, you have an algorithmic authority problem. Repeat the test with Perplexity AI and Google’s Gemini. Compare results across all three. The full 5-minute diagnostic is here →
What is semantic drift and why does it matter?
Semantic Drift is the use of multiple inconsistent terms for the same concept across your digital surfaces. Example: your website says “platform,” your LinkedIn says “solutions provider,” your sales deck says “partner,” and your pitch materials say “tool.” Each term places you in a different category from AI’s perspective. In 50+ audits, Semantic Drift appeared as the primary AI classification failure in 80% of companies. Read the full semantic drift diagnostic →
What is the Algorithmic Authority Stack?
The Algorithmic Authority Stack is a 7-layer diagnostic framework for AI visibility, developed by Maria Dykstra after auditing 50+ B2B companies. The seven layers are: Layer 1 (Market Identity Clarity), Layer 2 (Expertise Architecture), Layer 3 (Semantic Density), Layer 4 (Training-Ready Content), Layer 5 (Algorithmic Touchpoint Presence), Layer 6 (Trust & Proof Signals), and Layer 7 (Visibility Measurement). Each layer is independently testable and breaks independently. Full breakdown of all 7 layers →
How long does it take to fix AI invisibility?
It depends on which layers are broken. One Series B SaaS company went from zero Perplexity AI citations to eleven in 30 days by restructuring their digital presence across Layers 1-4. They didn’t rebrand or redesign. They restructured what they already had. Identity clarity (Layer 1) and semantic consistency (Layer 3) typically produce the fastest returns. Citation building (Layer 6) takes 3-6 months. See the full list of guides for building and fixing your AI visibility →
What does the Algorithmic Authority Audit cost?
The Algorithmic Authority Audit starts at $4,500 for the diagnostic. The Audit + Content Engine package is $12,000. Full System Build engagements are custom-priced. See all three tiers →
Is your company invisible to AI?
Six questions, about 90 seconds. Find out which of the seven layers is breaking first, and whether you are failing to be retrieved or failing to be cited.
Take the AI Visibility Test
