AI VISIBILITY AUDITS & SCORING
Measure. Optimise. Amplify—Turn AI outputs into measurable impact.
Pain Point
Tracking AI visibility can feel like a guessing game. Different teams measure success differently, and it’s hard to know which AI outputs are truly driving relevance, authority, and conversion. Without a standard approach, you risk wasted effort, inconsistent results, and missed opportunities.
Our Solution
Introducing AI Visibility Scoring Frameworks—a standardized, data-driven approach to measure and optimise your AI content and outputs. With a clear framework, every team can speak the same language and focus on what actually drives impact.
How We Do It
Define Your Scores: We create a composite scoring system across relevance, authority, and conversion, tailored to your business priorities.
Audit Existing AI Outputs: We evaluate every piece of AI-generated content, listings, or recommendations against the framework.
Optimise & Iterate: Using insights from the scoring, we refine your AI outputs to boost relevance, enhance credibility, and increase conversions.
Automate & Monitor: Continuous measurement ensures AI visibility grows sustainably over time.
What You Get
A single, unified visibility score for all AI outputs.
Clear visibility into where your AI excels and where it underperforms.
Prioritized action plans for optimisation based on data.
A repeatable, scalable system to monitor and improve AI visibility across teams.
The Outcome
Transform AI visibility from guesswork into measurable growth. Your AI outputs become more relevant, authoritative, and conversion-focused—driving real business impact while giving your teams confidence in every decision.
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Share of Voice
Benchmark your brand’s presence across Search.
AI is now the discovery layer. Customers rely on Google’s AI Overviews, Amazon’s Rufus, ChatGPT, and Copilot to make decisions long before they reach your website.
But in an AI-driven world, rankings no longer tell you whether AI systems talk about you at all.
AI Growth Hub’s Share of Voice Audit gives you a precise, data-driven benchmark of how often your brand is mentioned, recommended, or excluded across search engines and large language models — and reveals the technical, content, and entity factors shaping your visibility.
This is the foundation of any serious AI visibility strategy.
THE PAIN POINT
Your brand may have market presence, category authority, and strong SEO — yet still be invisible to AI systems.
Common issues we uncover include:
LLMs recommending competitors but not mentioning your brand
Google’s AI Overviews omitting you entirely
Amazon Rufus pushing alternative products
LLMs relying on outdated or inaccurate data about your brand
Weak structured data or missing entity definition
Content that models cannot extract, understand, or cite
Competitors overweighted due to stronger publisher or Wikipedia presence
If AI systems don’t “know” your brand, customers never see you — no matter how strong your traditional SEO is.
Your visibility in LLMs is now a competitive advantage.
And right now, you don't know what you’re missing.
THE SOLUTION
The Share of Voice Audit provides a complete visibility baseline across search AI and large language models.
You’ll see exactly:
When and where you appear in AI-generated answers
How often competitors are recommended instead
What data sources models pull from
Where your entity signals are missing or weak
Why models include or exclude your brand
Which actions will increase your AI visibility the fastest
This audit replaces guesswork with clarity — giving you the intelligence needed to win in the AI discovery ecosystem.
HOW WE DO IT
Our methodology combines technical AI SEO, entity engineering, and model-specific evaluation.
1. AI Share of Voice Analysis
We test your target category, product, and brand queries across:
Google Search + AI Overviews
Bing + Copilot
Amazon search + Rufus
Walmart / Instacart / vertical retail search
ChatGPT, Claude, Gemini, Perplexity
We measure mention frequency, order, omission rate, and competitor share.
2. Entity & Data Layer Audit
We evaluate the technical backbone models rely on:
Schema & structured data depth
Product metadata
Wikipedia & Wikidata alignment
Publisher authority footprint
Entity completeness and consistency
LLM-friendly content availability
3. Citation & Source Mapping
We identify the exact domains, documents, and sources that models are using to make recommendations — and where your brand is missing.
4. AI Red-Team Testing
We stress-test each model for:
Hallucinations
Outdated information
Misattributed brand details
Compliance, medical, or reputational risks
5. Roadmap & Visibility Plan
We translate the findings into a clear 30/60/90-day plan that prioritises the biggest visibility wins.
WHAT YOU GET
The deliverables are built for senior technical and digital leaders who need clarity and immediate actionability.
You receive:
A full Share of Voice benchmark across all major LLMs and search AI
Competitive visibility analysis
Entity and structured data audit
Publisher and citation gap analysis
AI hallucination and risk report
Detailed 30/60/90-day roadmap
Executive summary for CTO/CISO/VP Digital stakeholders
Everything is designed to drive visibility, reduce risk, and enhance brand accuracy across AI systems.
THE OUTCOME
With a Share of Voice Audit, you move from uncertainty to control.
You will:
Understand your real visibility across AI systems
Know exactly why competitors are outranking or replacing you
Improve inclusion in LLM-generated answers
Strengthen your entity signals across search and AI ecosystems
Reduce hallucination and compliance risk
Align teams around a clear AI visibility strategy
Build a defensible position in category-defining AI surfaces
AI discovery is now the battleground for customer attention.
This audit ensures your brand isn’t invisible in the systems shaping decisions.
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AI VISIBILITY DASHBOARDS
Unified AI Visibility across every major LLM
Pain Point
In today’s AI-driven world, your brand and content can appear across multiple large language models—ChatGPT, Gemini, Perplexity, Copilot—but tracking performance is scattered, inconsistent, and time-consuming. Without a unified view, you’re left guessing which content works, which prompts drive results, and where opportunities are slipping away.
Our Solution
Cross-LLM Insights centralizes your AI visibility, giving you a single source of truth across all major LLMs. We aggregate, standardize, and analyze metrics so you can understand exactly how your content performs—anywhere AI engages your audience.
How We Do It
Aggregate Metrics: Pull data from ChatGPT, Gemini, Perplexity, Copilot, and other emerging LLMs.
Standardize Visibility: Normalize outputs across platforms for easy comparison.
Analyze Patterns: Identify top-performing prompts, content formats, and visibility gaps.
Actionable Dashboards: Provide real-time insights, alerts, and recommendations.
What You Get
A centralized dashboard of AI visibility metrics across all major LLMs
Performance reports highlighting trends, opportunities, and risks
Data-driven insights to optimize content and prompts for maximum reach
Alerts and notifications for sudden shifts in AI visibility
The Outcome
With Cross-LLM Insights, you move from fragmented, reactive tracking to proactive, strategic AI visibility management. You’ll know what’s working, make informed decisions faster, and ensure your brand and content consistently appear where it matters—across every major LLM.
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