AI Visibility Audit: See How AI Assistants Find and Recommend Your Company
Measure how AI assistants understand, cite and recommend your company—and turn the gaps into an actionable GEO roadmap.
Buyers increasingly ask AI assistants which suppliers, products and technologies fit their needs. RFQmatch audits your visibility across major AI answer engines, identifies factual and semantic gaps, and prioritizes the content, entity and structured-data changes most likely to improve discoverability.
What is AI Visibility Audit?
Assesses how well an organization, its products and expertise are understood, retrieved and recommended by AI assistants such as ChatGPT, Claude, Gemini, Copilot and Perplexity. Evaluates semantic authority, structured data, entity recognition, knowledge graph presence and AI-generated answers, then provides a prioritized roadmap to improve AI discoverability and recommendation quality.
The problem this solves
Organizations are not accurately represented or recommended by AI assistants, causing lost visibility, reduced trust and fewer qualified procurement opportunities.
Symptoms you may recognise
- Search invisibility: Do you notice that when prospects ask ChatGPT or Perplexity for vendors in your category, your company does not appear at all?
- Wrong descriptions: Do you see AI tools describing your products, services, or expertise with outdated, incomplete, or incorrect details?
- Competitor preference: Do you find that AI assistants recommend competitors first, even when your offering is stronger or more relevant?
- Inconsistent answers: Do you experience different AI-generated summaries depending on the assistant, with no clear or stable version of your business story?
- Hidden expertise: Do you notice that your thought leadership, case studies, and subject-matter expertise are rarely surfaced in AI answers even when they are publicly available?
KPIs that deteriorate
- Lead quality: Do you see more unqualified leads because AI summaries set the wrong expectation before the first sales conversation?
- Organic traffic: Do you notice declining clicks and site visits because users get their answers directly from AI without naming your company?
- Conversion rate: Do you experience lower inquiry-to-opportunity conversion because prospects compare you against inaccurate AI-generated alternatives?
- Win rate: Do you see deal losses where buyers mention a competitor that AI presented more prominently or more credibly than you?
- Brand search: Do you notice falling branded search volume or weaker direct navigation as fewer users remember or recall your company name?
Business risks
- Category exclusion: Do you worry that your organization becomes absent from the AI layer that buyers now use to shortlist suppliers?
- Reputation drift: Do you see a risk that repeated AI inaccuracies harden into a market narrative you no longer control?
- Revenue leakage: Do you fear losing pipeline because AI assistants steer buyers toward better-represented competitors?
- Trust erosion: Do you notice that customers start questioning your credibility when AI summaries contradict your website or sales materials?
- Strategic blind spot: Do you worry that leadership is investing in content, SEO, and thought leadership without knowing whether AI systems can actually use it?
Typical trigger events
- AI rejection: Do you start looking after a major account says an AI assistant did not mention your company in its vendor shortlist?
- Competitor mention: Do you react when a competitor is repeatedly recommended by AI tools for work that you clearly also do?
- Board question: Do you begin searching after the board or CEO asks why your brand is missing from AI-generated category answers?
- Launch disappointment: Do you notice a new product, service, or rebrand is not being reflected in AI answers weeks or months after launch?
- Sales escalation: Do you get triggered by repeated sales escalations about prospects quoting wrong AI information during discovery calls?
Who this service is for
Organisation size
50-200 · 200-1000 · 1000-10000 employees — 10M-50M USD, 50M-250M USD, 250M-1B+ USD
Company maturity
Scale-up, Enterprise, Multinational
Industry verticals
Technology and Software, Financial Services, Healthcare and Life Sciences, Professional Services, Retail and E-commerce
Typical buyers
- Chief Marketing Officer (CMO) — Decision Maker
- VP of Digital Marketing / Corporate Communications — Decision Maker
- Head of Search Engine Optimization (SEO) / GEO — Influencer
What RFQmatch delivers
Deliverables
- AI Search Engine Share-of-Voice Baseline Assessment Report.
- Semantic Authority and Knowledge Graph Gap Analysis.
- Structured Data & Schema.org Optimization Roadmap.
- Answer Engine Optimization (AEO) Content Strategy Playbook.
- AI Visibility Tracking Dashboard and GA4 Custom Reporting Configuration Framework.
Business outcomes
- Increased likelihood of brand inclusion in AI assistant purchase recommendations and organic comparisons.
- Reduction in brand reputation risk caused by hallucinated or inaccurate product facts surfacing in AI answers.
- Measurable growth in highly qualified organic web traffic driven by AI search engine citations.
- Future-proofed content infrastructure ready for the long-term shift from standard search to answer engines.
- Improved marketing efficiency by aligning PR, technical SEO, and content creation under a single semantic framework.
Expected ROI
- 35% increase in citation share across leading AI search assistants
- Significant lift in brand sentiment scores within generative engine text outputs
- 20-30% improvement in conversational search referral traffic volume
- 100% elimination of critical factual errors generated by major AI systems within 90 days
- Measurable cost efficiencies gained by pivoting from outdated legacy SEO tactics
How the engagement works
- 1
Phase 1: Discovery & AI Search Benchmarking
Conduct extensive query testing across target LLMs to establish baseline brand visibility, identify misrepresentations, and evaluate competitor share-of-voice.
- 2
Phase 2: Technical & Semantic Audit
Analyze technical assets, structured schema data, website architecture, and external knowledge graph listings (Wikidata, industry databases) for AI ingestion readiness.
- 3
Phase 3: Strategy & Playbook Development
Design an actionable optimization playbook focusing on high-authority citations, structural content formatting, and entity recognition enhancement.
- 4
Phase 4: Implementation Enablement & Governance
Host training workshops for marketing, SEO, and engineering teams to integrate AI-first content principles and set up ongoing tracking mechanisms.
Small project
4 - 6 weeks
Medium project
8 - 12 weeks
Large project
14 - 18 weeks
Quick Scan
A rapid 4-week diagnostic focused strictly on core brand visibility metrics across 3 main AI assistants and a high-level gap summary.
Best for: Organizations requiring immediate, low-cost visibility into their baseline AI exposure before structuring a full program.
Pilot
An 8-week deep-dive covering one specific product line or business unit, delivering a tailored technical audit and immediate remediation steps.
Best for: Multi-brand companies wanting to prove the value of AEO modifications on a single asset before scaling globally.
Full Implementation
A comprehensive 12-week enterprise-wide program analyzing all product lines, technical infrastructures, knowledge graph footprints, and establishing internal AEO governance.
Best for: Market leaders facing immediate pressure from AI search displacement who require cross-departmental execution roadmaps.
Data and systems required
- Website
- CMS
- Search Console
- Bing Webmaster Tools
- Schema.org markup
- AI assistant responses
- product catalog
- company profile
- analytics
- knowledge graph entities
Scope and pricing
AI Visibility Audit
From €4,500 (indicative; scope varies by markets, brands, products and query set)
What's included
- Priority-query design
- multi-engine baseline testing
- brand/entity consistency review
- citation/source analysis
- structured-data review
- competitor comparison
- visibility scorecard
- prioritized GEO remediation roadmap.
Not included
- Guaranteed rankings/citations
- direct modification of closed AI models
- large-scale website rewrite
- ongoing monitoring
- PR/backlink campaigns
- paid media.
Why RFQmatch
RFQmatch AI Visibility Baseline
RFQmatch frames AI visibility around real B2B supplier-selection questions rather than generic brand mentions; can connect company/product data quality to discoverability; combines prompt testing with structured-data and entity analysis; produces a remediation backlog rather than only a score.
- B2B procurement-oriented query sets
- supplier/product entity focus
- combines AI-answer testing, source/citation analysis and structured-data review
- separates visibility from factual accuracy
- designed to lead into measurable ongoing monitoring.
Related services
- GEO Optimization; AI Visibility Monitoring; Structured Data Implementation; Metadata Engineering; Supplier Digital Presence Audit.
Frequently asked questions
What is an AI Visibility Audit?
A structured measurement of how AI assistants describe, cite and recommend a company or its products for relevant buyer questions, combined with analysis of the sources and machine-readable signals behind those answers.
How is an AI Visibility Audit different from SEO?
SEO focuses primarily on search-engine crawling, indexing and rankings
Can you guarantee ChatGPT will recommend my company?
an AI visibility audit additionally evaluates generated answers, citations, entity understanding and recommendation context.
What is measured?
No. Closed AI systems and retrieval mechanisms cannot be controlled or guaranteed. The service identifies factors the organization can improve and measures observable outcomes.
What happens after the audit?
Typical measures include recommendation share, citation frequency, factual accuracy, competitor presence, source patterns and consistency across query variants.
Ready to get started?
Tell us about your situation and we'll help you scope the right engagement.
Request an AI Visibility Audit