The Zebora Approach

Strategic Intelligence for Brand Visibility in the Age of AI

About Zebora

Zebora is a strategic consultancy and proprietary analytics platform that helps brands measure, understand, and optimize how they're represented inside large language models (LLMs) like ChatGPT, Claude, and Gemini.

We combine hands-on strategic consulting with advanced technology to deliver actionable intelligence that drives real commercial outcomes. Unlike automated Generative Engine Optimisation (GEO) tools that provide dashboards without context, Zebora delivers both the data and the strategy to act on it.

Founded: 2025
Headquarters: UK, with operations across US, Europe, and Australia
Focus: LLM Brand Intelligence for CMOs and brand leaders
Approach: Hybrid consultancy + proprietary platform

1. The New Battleground for Brand Visibility

Over the last decade, marketing teams mastered the art of the website and the rules of search. SEO became a science, paid search became predictable, and social listening kept brands attuned to public sentiment.

But the battleground has shifted. Discovery, comparisons, and recommendations are now happening inside AI models — not just on Google or social media. When someone asks ChatGPT, Claude, or Gemini for "the best mortgage provider," "alternatives to Notion," or "how sustainable is brand [X]?", the answers shape real decision making.

The shift is already happening:

• Retail website traffic from LLM sources increased 3,500% in the past year (Adobe Analytics)
• 80% of consumers now rely on AI summaries for at least 40% of their searches (Bain & Co)
• Organic web traffic is projected to decline 50% by 2028 (Gartner)

If your brand isn't visible in LLM answers today, you won't exist in the discovery layer of tomorrow.

For brands, this marks a profound change: We're rapidly moving towards a zero-click world. And in 2-3 years, the main purpose of your website won't be human navigation — it will be to feed the LLMs.

Yet most marketing and brand leaders have no visibility into how these systems perceive them, rank them, or recommend them. Or whether they're turning up at all. Brands need AI-powered brand intelligence to understand how ChatGPT, Claude, and other generative AI platforms shape their reputation.

2. The Visibility Gap: What the Market Gets Wrong

“GEO” (Generative Engine Optimization) is full of hype, half-baked data, and misleading metrics. Many so-called "SEO for AI" tools rely on vanity numbers or oversimplified methods that don't reflect real-world user intent or behaviour. The fundamental misunderstanding: most vendors treat GEO like SEO with a new acronym—optimizing for keywords. But LLMs don't work that way. Real LLM optimization requires understanding language, context, and the intent behind real customer questions.

The result:

• Low transparency: No one knows where model outputs come from
• Misleading metrics: Numbers with no real meaning or commercial relevance
• Dashboard fatigue: Data without strategy, insights without action
• One-size-fits-all: Generic prompts that don't reflect how your customers actually search
• The SEO fallacy: GEO is not just "SEO for AI." LLMs don't work like search engines—they synthesize narratives from multiple sources, not rank pages by backlinks. Treating LLM optimization like keyword stuffing misses the point entirely.

The fundamental problem: Most GEO tools are built by data scientists for data scientists, not for marketers who need to drive business outcomes.

At Zebora, we take a different approach. We replicate how real people use LLMs — and we show brands how they actually appear in those conversations.

Key Insight: We don't just measure ChatGPT brand visibility — we see how your customers see you.

3. The Zebora Difference: Consultancy + Platform

Zebora bridges the gap between AI perception and commercial impact, translating visibility data into actionable growth plans that improve sales, trust, and market share.

Why Consultancy Matters

Off-the-shelf GEO tools can't solve brand visibility. Here's why:

Our consulting process:

Phase 1: Discovery & Taxonomy Design (Week 1-2)
We audit your category, competitors, and business priorities. We interview your team to understand what drives revenue, which customer segments matter most, and where you're trying to differentiate. Then we design 500+ prompts that mirror real customer behavior in your specific market.

Phase 2: Baseline Analysis (Week 3-4)
We run your brand and competitors through our proprietary platform across multiple LLMs. We don't just count mentions—we analyze positioning, sentiment, context, and competitive framing. We also audit your website and content to identify gaps and opportunities.

Phase 3: Strategic Diagnosis (Week 5)
We identify the gaps: Where are you invisible? Where are competitors winning? What signals are LLMs missing? Which external sources are influencing model outputs? What misconceptions exist about your brand?

Phase 4: Action Planning (Week 6-8)
We map the sources LLMs trust, audit your digital footprint across those sources, and build a prioritized roadmap to close visibility gaps and strengthen your narrative. This includes content strategy, structured data recommendations, and third-party source optimization.

Phase 5: Ongoing Tracking (Monthly/Quarterly)
You get platform access to monitor shifts in visibility, sentiment, and competitive position—with quarterly strategic reviews to adapt your approach as the landscape evolves.

Why Our Platform Matters

Strategic consulting without rigorous data is just expensive guesswork. Our proprietary platform is what makes our depth of insight possible and unrivalled.

Platform capabilities:

Custom Prompt Taxonomy
500+ curated prompts per client that simulate 85-90% of all real-world, high-intent searches in your category. These aren't generic "best [category]" queries—they're mapped to the six dimensions of how customers actually search:

• Overall category (e.g., "best mortgage provider")
• Product types (e.g., "best fixed rate mortgage")
• Product features (e.g., "noise cancelling headphones with longest battery life")
• Differentiators/USPs (e.g., "fastest home broadband providers")
• Customer segments (e.g., "best SUV for a large family")
• Use cases (e.g., "Best CRM for remote teams")

Multi-LLM Data Collection
We track your brand across ChatGPT (GPT5), with Claude and Gemini integration coming soon. This already covers ~90% of the real AI discovery market—mirroring how your customers are actually using AI tools.

Intelligent Scoring Engine
Our semantic and sentiment analysis goes far beyond mention counting. We assess:

• Recommendation strength (Is your brand presented as the clear choice, one option among many, or not mentioned?)
• Contextual framing (Are you positioned correctly for the query intent?)
• Competitive positioning (How are you ranked relative to alternatives?)
• Sentiment drivers (What specific factors influence positive or negative framing?)

Signal Source Mapping
We identify and categorize every external URL and domain cited by the models—review sites, publishers, brand-owned content, forums, Wikipedia, knowledge graphs. This reveals which sources are shaping LLM perceptions of your brand.

Early Warning System
Automated alerts detect sentiment drift, visibility loss, competitive disruption, and emerging threats—appearing directly in your dashboard so you can act before problems compound.

The result: A closed-loop system designed to mirror real user behavior and deliver insights that drive commercial decisions.

Key Insight: This makes Zebora the only system that measures brands as your customers experience them inside the model—not just what the model says about you when directly asked.

4. The Zebora Brand AI Intelligence Framework

What is LLM Brand Intelligence?
LLM Brand Intelligence measures how large language models perceive, position, and recommend brands across real-world user prompts. Unlike traditional brand tracking (surveys, focus groups) or digital analytics (clicks, impressions), LLM Brand Intelligence reveals how AI systems shape purchase decisions in a zero-click world.

Our framework is applied at both brand and category levels—for example, within mortgages rather than the entire financial services landscape. This ensures relevance and actionable insights.

The Three Pillars of LLM Brand Intelligence 

We've condensed our methodology into three core pillars:

Pillar 1: VISIBILITY

What it measures: How often and where your brand appears across real-world, high-intent user prompts.

How it works:

AI Share of Voice
Your brand's ownership of the AI conversation in your category. It answers: "Out of everything people could ask AI about our category where we're relevant, what percentage of those answers include us?"

Measured across six dimensions with business-aware weighting that reflects commercial impact:

• Overall category mentions
• Product type visibility
• Feature-based searches
• USP/differentiator queries
• Customer segment targeting
• Use case relevance

Output: A percentage score showing what share of all AI-driven, high-intent searches your brand effectively owns—enabling you to benchmark, track growth, and spot gaps before competitors fill them.

Competitor Ranking
A ranked leaderboard using both visibility weighting (is your brand mentioned?) and mention position (were you first, second, third?). Can be filtered by product type, segment, or use case—showing exactly where you're outperforming or losing ground.

Why it matters commercially:
If you're invisible in 40% of high-intent prompts where you're relevant, you're losing 40% of potential consideration. Visibility is the prerequisite for everything else.

Pillar 2: INFLUENCE

What it measures: How strongly LLMs endorse, prefer, or recommend your brand relative to competitors.

How it works:

LLM Recommendation Score
Because most LLMs default to neutral positivity ("Brand X is a good choice among others"), simple tone analysis isn't enough. We assess the strength, clarity, and conviction of model recommendations through 200+ customer-style prompts that fully interrogate each LLM.

We score every mention on a 5-point scale:

• 5 – A clear top choice
• 4 – A recommended option
• 3 – One of several viable options
• 2 – A weak inclusion
• 1 – A negative example

Example:
"Nationwide is a solid option for first-time buyers, though some may prefer smaller lenders for flexibility."
→ Score: 3.5 — Viable/Mixed Review

Sentiment Scorecard
Our framework evaluates tone, positioning, caveats, and fit—mirroring how a human would interpret model recommendations. We also highlight the top five positive and negative drivers shaping your sentiment.

Why it matters commercially:
Visibility without endorsement doesn't drive conversion. If your brand appears but LLMs frame competitors as the better option, you've lost out.

Pillar 3: UNDERSTANDING

What it measures: How accurately and completely LLMs understand your brand, products, and positioning.

How it works:

Brand Perception Scorecards
We assess three layers (all scored 1-5):

1. Core Brand Pillars — Authority, Trust, Clarity
2. Authority: Is your brand presented as expert or leading?
3. Trust: Is your brand shown as reliable and safe?
4. Clarity: Can the LLM clearly explain what you do and for whom?
5. Brand-Specific Values — Unique to each brand’s values, evaluated for spontaneous salience
   (e.g., for a sustainability-focused brand: "Does the LLM associate us with environmental leadership?")
6. Product Understanding — How fully the model recognizes your products, features, and target audiences

Critical Insight: When LLMs choose which brands to surface or recommend, they implicitly reward those with the highest Authority, Trust, and Clarity. And they have to understand your products if they are going to mention them. So measuring these signals is the foundation of sustainable LLM visibility. 

Brand Accuracy Index
A single, unified metric that captures overall brand health inside LLMs using weighted scores across all three perception layers.

Measured monthly, the Brand Accuracy Index reveals shifts in brand health—helping teams detect early changes in visibility, sentiment, or competitive momentum.

Why it matters commercially:
If LLMs don't understand what you do, who you serve, or why you're different, they can't recommend you correctly. Misunderstanding leads to misalignment—your brand appearing in the wrong contexts or missing from the right ones.

5. How Zebora Works: End-to-End Process

Step 1: Prompt Taxonomy Development
500+ prompts simulate real, high-intent user behavior across categories, features, use cases, and customer segments. These are designed collaboratively with your team to ensure business relevance.

Step 2: Multi-LLM Data Collection
We run your prompts across ChatGPT 5, with Claude and Gemini coming soon—covering 80-90% of the real AI discovery market.

Step 3: Intelligent Scoring
Semantic and sentiment analysis with custom weightings unique to your business. Our scoring engine evaluates not just mentions, but context, framing, and competitive positioning.

Step 4: Signal Source Mapping
We identify every external URL and domain cited by the models, categorized by source type:

• Review sites (G2, Trustpilot, Capterra)
• Publishers (Forbes, TechCrunch, industry media)
• Brand-owned content (your website, blog, press releases)
• Forums (Reddit, Quora)
• Knowledge graphs (Wikipedia, Wikidata)
• Structured data sources

This reveals which sources are shaping LLM perceptions—and where you need to strengthen your signal.

Step 5: Strategic Analysis & Recommendations
We deliver:

• Executive summary with key findings and commercial implications
• Detailed visibility, influence, and understanding scores
• Competitive benchmarking across all dimensions
• Gap analysis showing where and why you're losing to competitors
• Prioritized action plan with specific recommendations
• Source optimization strategy targeting high-impact signals
• Technical and content audits, recommendations and action plans.

Step 6: Ongoing Monitoring & Optimization
Monthly dashboard updates track visibility shifts, sentiment changes, and competitive movements. Quarterly strategic reviews ensure your approach evolves with the landscape.

All of this intelligence comes from a closed-loop system designed to mirror real user behavior—not hypothetical queries or academic research.

6. Example in Action: Nationwide Mortgages

Category: UK mortgage providers

Challenge:
Nationwide wanted to understand how LLMs were representing their mortgage products relative to competitors, and whether they were visible in the contexts that drive revenue.

What we found:

Visibility: Nationwide ranked consistently #2 across the majority of mortgage-related prompts, with strong presence in "first-time buyer" and "best rates" contexts.

The Gap: Nationwide significantly underperformed in three key segments: interest-only mortgages, home movers, and buy-to-let for landlords. Despite offering these products, LLMs rarely mentioned Nationwide in these contexts—a blind spot worth millions in potential lending.

Root Cause Analysis:
Our signal source mapping revealed that Nationwide's buy-to-let products lacked sufficient presence in the review sites, comparison platforms, and publisher content that LLMs rely on for landlord-focused recommendations. Competitor marketing had saturated these channels, while Nationwide's content focused heavily on first-time buyers.

Strategic Recommendation:

1. Optimize core product pages with clearer buy-to-let positioning and landlord-specific features
2. Strengthen presence on landlord-focused review platforms and comparison sites
3. Develop targeted content for publisher partnerships and third-party sites
4. Update structured data and knowledge graph entries to reflect full product range
5. Monitor monthly for visibility improvements in landlord and interest-only segments

The Insight:
Even strong brands have blind spots in LLM visibility. Without measurement, these gaps compound—allowing competitors to own valuable contexts by default.

7. The Bigger Picture: Why This Matters Now

The way LLMs talk about your brand already influences purchasing decisions, PR narratives, investor sentiment, and talent recruitment.

For CMOs: Your brand equity is shifting from "share of search" to "share of answer." If LLMs don't recommend you, you're invisible to the next generation of customers.

For CEOs: AI-powered discovery is becoming the default. Brands that aren't investing in LLM visibility today will spend 2026-2028 playing catch-up while competitors own the narrative.

For CFOs: The cost of inaction is measurable. If 50% of your organic traffic disappears by 2028 (Gartner's projection), what's the revenue impact? What's the customer acquisition cost without that visibility?

Traditional brand tracking doesn't capture this shift. SEO, social listening, and performance marketing analytics measure human clicks and impressions. Zebora measures visibility and influence in the machines that shape those human decisions—in a zero-click world where answers replace links.

8. What You Can Do With Zebora

Our clients use Zebora intelligence to:

✓ See exactly how LLMs perceive them across visibility, influence, and understanding
✓ Benchmark against competitors and category leaders on metrics that matter
✓ Identify where to intervene (missing signals, unclear messaging, low differentiation)
✓ Track visibility and sentiment shifts month over month with early warning alerts
✓ Take strategic action to strengthen their narrative inside AI before competitors do
✓ Optimize content and sources that LLMs trust and cite
✓ Protect brand equity from negative framing or competitive displacement
✓ Expand into new markets with confidence about how LLMs position them there

Zebora helps you take control of your brand narrative — before the models write it for you.

9. About the Team

Zebora was founded in 2025 by Liam and Grant, the team behind the award-winning growth marketing agency TrueUp and the AI-first tech jobs platform theround.com. With expertise spanning AI systems, brand strategy, and growth marketing, we've launched SMARTY for Three Mobile, run Telefonica’s most successful paid social campaigns to date and helped grow brands such as Atom Bank, The Economist, Vodafone and Quizlet.

Our mission is simple: Give brand leaders the same visibility and control inside LLMs that they once had in search.

We operate across the UK, US, Europe, and Australia, working with brands that understand the future of discovery isn't happening on websites—it's happening inside AI.

10. Frequently Asked Questions About Generative Engine Optimization (GEO)

Q: How is LLM brand intelligence different from traditional SEO?
A: Traditional SEO optimizes for search engine rankings and clicks. LLM Brand Intelligence optimizes how brands appear in AI-generated answers from ChatGPT, Claude, and Gemini—where no clicks occur. In this zero-click world, your brand's signals matter more than your website's ranking.

Q: How can I improve my brand's visibility in ChatGPT and other AI tools?
A: Improving brand visibility in ChatGPT requires mapping the sources LLMs trust (review sites, publishers, knowledge graphs) and strengthening those signals. We identify gaps in how AI perceives your brand, then optimize your content for AI comprehension, structured data, and the channels that influence model training.

Q: Which AI platforms do you track for brand visibility?
A: We currently track ChatGPT (GPT-5), covering 85-90% of real-world AI usage for brand research. Claude and Gemini integration coming soon. We focus on platforms your customers actually use, weighted by market share.

Q: Can you guarantee my brand will rank higher in AI-generated answers?
A: We identify exactly where and why LLMs aren't recommending your brand, then create a roadmap to close those gaps. Our approach is based on understanding what sources AI trusts and how to optimize them systematically. Most clients see measurable AI brand visibility improvements within 3-6 months.

Q: How long does it take to see results from AI search optimization?
A: Website optimization and structured data updates can appear in 4-6 weeks. Building authority through third-party citations typically takes 8-12 weeks. LLMs continuously retrain, and we provide monthly tracking so you can measure your brand's AI visibility improvements over time.

Q: Does LLM brand intelligence work for B2B companies and niche industries?
A: Yes. Business buyers increasingly use ChatGPT and Claude for vendor research, and professional gatekeepers—journalists, analysts, procurement teams—rely on AI tools to surface credible options. If your buyers use AI for research, you need visibility.

Q: How do I track my brand's performance in ChatGPT and other AI models?
A: Our platform runs 500+ high-intent prompts that mirror real customer behavior, scoring your brand on visibility, recommendation strength, and accuracy. You get a dashboard showing where you appear, how you're positioned versus competitors, and which gaps to address.

Q: What is generative engine optimization (GEO) and how does it work?
A: Generative Engine Optimization (GEO) optimizes how brands appear in large language models like ChatGPT, Claude, and Gemini. Unlike SEO's focus on keywords and backlinks, GEO requires semantic clarity, source authority, and narrative consistency across channels that train AI models.

Q: My industry isn't commonly searched in AI yet—should I still care?
A: Early investment in AI search optimization gives you first-mover advantage. We identify adjacent use cases where your brand should appear, positioning you to dominate as adoption accelerates. Plus, journalists and business buyers in your space already use AI for research.

Q: How does ChatGPT affect my SEO and organic traffic?
A: Gartner projects 50% decline in organic web traffic by 2028 as users get answers directly from AI instead of clicking websites. Traditional SEO metrics are becoming less relevant. The brands that win will optimize for AI-generated recommendations, not just Google rankings.

11. Glossary of Key Terms

LLM Brand Intelligence: The practice of measuring, analyzing, and optimizing how large language models perceive, position, and recommend brands across real-world user queries.

AI Share of Voice: The percentage of AI-generated responses in which a brand appears across all relevant, high-intent queries in their category.

Generative Engine Optimization (GEO): The practice of optimizing brand presence in large language models. Unlike SEO, which focuses on website rankings, GEO focuses on brand representation in AI-generated content. Also sometimes referred to as AEO (AI Engine Optimization)

Zero-Click World: A search environment where users receive answers directly from AI systems without clicking through to websites, fundamentally changing how brands achieve visibility.

Signal Source Mapping: The process of identifying and categorizing external sources (review sites, publishers, knowledge graphs) that influence how LLMs represent brands

12. Next Steps

Curious how your brand appears inside AI?

We offer a complimentary Brand Visibility Audit for qualified brands—a snapshot of how you rank against competitors in high-intent prompts across your category.

What you'll receive:

• Your current AI Share of Voice vs top 3 competitors
• Visibility gaps where you're absent but should appear
• Sample LLM Recommendation Scores showing how you're framed
• Initial signal source analysis

This audit typically reveals 2-3 critical blind spots you didn't know existed.

📧 Get your Brand Visibility Audit: [contact email]
🌐 Learn more: https://zebora.io 📅 Book a strategy call: [calendar link]

Zebora. Own your narrative. Win in AI.