Is Your Company Found in AI Search?
The GEO Grader is a free AI visibility diagnostic that scores how well ChatGPT, Claude, Gemini, Perplexity, and Grok recognize your brand, returning a 0-100 Entity Confidence score. Built for brands whose buyers now begin their research inside AI platforms before any decision is made.
What your score means
Critical
AI doesn't know you
Fragmented
Partial recognition
Dominant
AI recommends you
Understanding Your AI Visibility Score
Swipe to see all scores→
How This Tool Works
Under the hood: our 3-step AI visibility analysis
The Signal Check
We ping the live APIs of major LLMs (ChatGPT, Perplexity, Claude, and Gemini) with variation queries about your brand (e.g., "Who is [Brand]?", "Best [Service] in [City]").
Vector Analysis
Our engine analyzes the response for "Hallucinations" (false data) and "Entity Confidence" (how sure the AI is). We look for citations, accurate pricing, and service matching.
The Visibility Score
We aggregate these data points into a single 0-100 score. A low score means the AI models are failing to retrieve accurate data from their Knowledge Graph about you.
The Missing Layer in Your SEO Strategy
Traditional SEO builds your foundation - rankings, traffic, and authority. According to Gartner research, 50% of professional research now happens through AI assistants like ChatGPT, Perplexity, and Gemini that pull from knowledge graphs, not search results.
This creates a critical blind spot. Your SEO team optimizes for Google's crawlers, but LLMs like GPT-4, Claude, and Gemini build their understanding from a fundamentally different data pipeline - training corpora, knowledge graph entries, and entity relationships embedded in the model weights. A brand can rank #1 on Google for every target keyword and still be completely invisible when a buyer asks ChatGPT "Who is the best provider of [your service]?"
The GEO Grader exposes this gap by querying five major AI platforms simultaneously and measuring whether they recognize your brand, understand your services, and can accurately describe your value proposition. The result is a single 0-100 score that quantifies your AI visibility - the metric that traditional SEO tools cannot measure.
Brands that score below 50 are in a critical state: AI models either don't know they exist, or worse, hallucinate incorrect information about them. Brands scoring above 80 are "entity-resolved" - meaning AI systems confidently recommend them as primary solutions. The difference between these two states increasingly determines who wins the next generation of buyer research.
What Your Score Means: An Action Plan
A score without a plan is noise. Each range below corresponds to a distinct infrastructure state and a specific sequence of corrective actions our team executes on behalf of enterprise clients.
Critical: Entity Infrastructure Does Not Exist
A Critical score signals that AI platforms have no verified entity record for your brand. When a buyer queries ChatGPT or Perplexity for your service category, your brand is absent from the response set. In competitive markets, this means every AI-assisted research session produces a shortlist that excludes you entirely, regardless of how strong your traditional search rankings are.
The root cause is almost always an absence of structured entity signals across the web: no Wikipedia presence or Wikidata entry, no verified Google Knowledge Panel, no consistent structured data across authoritative citation sources, and content that describes what you do without establishing who you are as a recognized entity. AI models cannot confidently reference a brand they cannot resolve across multiple independent sources.
The remediation sequence for Critical-range brands is direct and measurable. Our team architects the entity layer from the ground up, beginning with the signals that carry the highest weight in LLM training corpora.
- Architect a Wikidata entity record with full attribute set: official name, website, founding date, industry classification, and geographic service area
- Engineer a structured JSON-LD identity layer across your homepage, About page, and primary service pages using Organization and LocalBusiness schema
- Execute a citation audit and build verified listings across 40+ industry directories that feed into AI training sources
- Publish a minimum of four authoritative long-form assets per month that establish topical depth, each citing primary-source data
- Secure at least two earned media placements in indexed publications to create third-party entity corroboration
Fragmented: Partial Recognition, Incomplete Authority
A Fragmented score indicates that AI models have partial awareness of your brand but cannot confidently resolve your full value proposition. This is a dangerous state that many brands misinterpret as acceptable. When an AI system has incomplete entity data, it will generate responses about your brand that are partially accurate, partially invented, or both. A buyer reading that output may form an incorrect impression of your capabilities, pricing, or geographic coverage.
Fragmented brands typically have some structured data deployed but are missing entity corroboration from independent sources. The AI can find your brand but cannot reconcile conflicting signals about what your brand actually does, who it serves, or what distinguishes it from competitors. This gap in entity confidence is the primary driver of AI hallucinations about mid-market brands.
The plan for Fragmented brands focuses on signal consistency and authority amplification. Every data point an AI model can find about your brand must say the same thing, in structured language the model can parse with high confidence.
- Conduct a full entity consistency audit across all indexed properties and correct any conflicting signals in NAP data, schema markup, and meta descriptions
- Engineer a topical authority map and fill all semantic gaps with structured content clusters that establish category leadership
- Architect a digital PR campaign targeting at least six domain-authority 50+ publications to create authoritative third-party corroboration
- Deploy FAQ and Speakable schema across all primary service pages to feed AI answer engines structured, citable responses
- Build and verify your Google Business Profile entity connection to create a direct Knowledge Graph anchor point
Dominant: Entity-Resolved, Defend and Expand
A Dominant score confirms that AI models recognize your brand as a primary entity in your category and can accurately describe your services, differentiators, and service area. This is the state where AI-assisted research actively works in your favor: when buyers ask an AI platform which firms they should evaluate, your brand appears in the response set with accurate, confident attribution.
Dominant status is not a static achievement. AI training cycles mean your entity score can erode as competitors build their own entity infrastructure and as your content landscape shifts. The mandate for Dominant brands is to deepen category authority, expand into adjacent entity categories, and create enough authoritative signal volume that your position becomes structurally difficult to displace.
Our team executes an ongoing competitive monitoring protocol for Dominant-range clients to measure share of AI-generated responses across key buyer queries and identify any erosion before it becomes a scoring problem.
- Execute a quarterly AI response audit across ChatGPT, Claude, Gemini, and Perplexity to measure share of voice in your category and detect hallucinations before they compound
- Architect entity expansion into adjacent service categories and geographic markets to capture adjacent AI-response opportunities
- Engineer a thought leadership content program that positions key principals as recognized entities in their own right, amplifying the brand's overall authority signal
- Build a systematic earned media cadence of at least two placements per month to maintain the volume of third-party corroboration that LLMs weight heavily
- Deploy competitor entity monitoring to identify when competing brands approach Dominant status and architect pre-emptive authority responses
Every score range has a clear remediation path. The question is whether your team has the infrastructure and expertise to execute it at the speed your competitive landscape demands.
See the Full AI Visibility ServiceFrequently Asked Questions
What does the GEO Grader measure?
Why is AI brand recognition important?
How can I improve my brand's AI visibility score?
How often is AI training data updated?
Why does ChatGPT say "I don't know" about my company?
What is Generative Engine Optimization (GEO)?
How do I fix a low score?
Does this impact my Google Rankings?
AI Search Terminology
- Large Language Model (LLM)
- The underlying neural network technology (like GPT-4 or Claude 3) that processes and generates human-like text based on vast training datasets.
- Knowledge Graph
- A structured database of facts (entities) and their relationships. Search engines and AI use this to understand that "Apple" is a company, not just a fruit.
- AI Hallucination
- A phenomenon where an AI model confidently generates false or misleading information because it lacks a verified "ground truth" for a specific entity.
- Vector Database
- A storage method that indexes content by "meaning" rather than keywords, allowing AI to find relevant answers even without exact keyword matches.
- Entity Authority
- A measure of how confident an AI model is in the existence and attributes of a specific brand or concept within its training data.
- Retrieval-Augmented Generation (RAG)
- A technique where an AI fetches fresh data from a trusted source (like your website) before generating an answer, reducing hallucinations.
What is Generative Engine Optimization (GEO)?
Definition
Generative Engine Optimization (GEO) is an emerging optimization discipline that ensures brands appear in AI-generated answers from platforms like ChatGPT, Perplexity, and Google AI Overviews through structured content, authoritative sourcing, and entity optimization.
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