Audit & Tools / AI Visibility Audit
AI Visibility Audit: Generative Engine Optimization (GEO) Diagnostics
Most teams have no idea whether ChatGPT, Gemini, Claude, or Perplexity currently recommend their brand. An advanced AI Visibility Audit maps your semantic footprints, replacing traditional keyword trackers with hard, vector-based diagnostic proof.
Strategic Metrics
Our specialized Answer Engine Optimization diagnostic platform executes real-world target prompt flows across major conversational models, tracking whether your domain is surfaced, measuring reference metrics, and contrasting performance parameters directly with named category competitors.
It executes real-world target prompt flows across major conversational platforms, tracking whether your company domain is surfaced, measuring reference health parameters, and contrasting data metrics directly with named category competitors.
Director’s Growth Foundations
- Multi-Model Presence Mapping: Track branded, unbranded, and categorical references to discover real context index parameters across top models.
- Sentiment and Citation Audit: Validate whether current LLM output matrices provide accurate data points regarding your solutions.
- Gap Remediation Architecture: Identify the exact semantic structured content missing from your templates to trigger automated citations.
What happens without an Answer Engine Optimization framework
Brands Remain Invisible
Your technical infrastructure and content assets remain locked away from conversational model scrapers, leaking traffic to optimized players.
Budgets Target Guesswork
Without hard quantitative retrieval scores, growth capital is poured into unverified standard keyword lists that fail to alter citation summaries.
Context Mappings Break
Disjointed structured schemas and schema errors cause vector retrieval frameworks to misrepresent your platform options across discovery loops.
Everything the AI Visibility Audit evaluates
Multi-Model Presence Tracking
Simulates real-world user prompt environments across ChatGPT, Gemini, Claude, and Perplexity to identify structural citation nodes.
Factual Sentiment Check
Evaluates whether current engine generation records represent your system metrics accurately, tracing model citation source health.
Share of Voice Comparison
Benchmarks your retrieval weight head-to-head with up to 3 industry competitors using matching dataset test profiles.
Technical Entity Diagnostics
Surfaces schema fragmentation, context gaps, and data blocks currently causing algorithms to drop your root pages.
AI Visibility Audit Capital Allocation Mapping
Stop financing invisible technical debt. Align crawl metrics, link networks, and semantic content layers to clean algorithmic structures before spending digital marketing capitals.
From setup to a prioritized baseline
Step 1 — Define prompt parameters
Step 2 — Execute multi-engine queries
Step 3 — Extract competitive comparisons
Questions about the AI Visibility Audit
Most audits are processed and delivered within a few business days once the core target prompt matrix and competitor accounts are locked in.
The initial audit establishes your point-in-time performance baseline. Continuous, real-time tracking loops are managed natively by upgrading directly to the automated Visibility OS layer.
Standard SEO focuses entirely on keyword variations and blue-link click indices. AI Visibility Audits measure vector-based retrieval probability, contextual citations, and model recommendation hierarchies.
Our systems analyze your target audience, extracting a curated set of branded queries, high-value category-defining questions, and transactional consideration prompts that buyers plug into LLMs.
Request your AI Visibility Audit.
Tell us a bit about your business and we’ll run this diagnostic on your actual pages and prompts.