Question 1

What actions can brands take to increase visibility in LLMs?

Accepted Answer

Strengthen the signals models rely on—and the clarity of your story.
- Content: create/refresh authoritative pages; close narrative gaps; publish use-case guides and comparisons.
- Knowledge signals: Wikipedia/Wikidata hygiene; structured data/schema; consistent entity/product data.
- Proof: credible reviews, case studies, analyst/press coverage, partner citations.
- Technical IA: page/URL structure, internal links, clear entity naming.
Zebora prioritises and executes via proven frameworks—we don't game models; we improve the truth they see.

Question 2

What makes Zebora different from other AI & GEO dashboards?

Accepted Answer

Zebora is a consultancy + platform that measures and optimises how brands appear and are recommended inside ChatGPT, Claude, and Gemini—then turns findings into action.
- Narrative-driven: we surface the signals that matter, with drill-down detail available in an intuitive UI.
- Configured to your business: products, features, differentiators, segments, and brand attributes inform testing.
- Proprietary scorecard: Visibility (BVI), Recommendation (LRS), and Perception (Authority, Trustworthiness, Clarity, Relevance, Differentiation).
- Real user-intent + diagnostic prompts, with weightings (not all prompts are equal).
- Ongoing tracking, competitor benchmarks, and drift alerts.
- Action layer: proven growth frameworks and a strategic playbook, guided by consultants.
Outcome: clarity + action—not just charts.

Question 3

Who is Zebora for?

Accepted Answer

Zebora is for large or scaling, commercially complex organisations where AI shapes how customers discover, compare and choose.
- CMOs and brand leaders: a clear answer to "what's our AI strategy?", and the plan behind it.
- Digital, SEO and brand leaders: the diagnosis behind the visibility data you already have.
- Agencies and consultancies: specialist GEO delivered behind your brand, or alongside you.
- Scaleups and growth brands: why AI recommends someone else, and what to change.
If you only need tracking, a visibility tool will serve you better.

Question 4

Why combine a consultancy with a platform?

Accepted Answer

Seeing the problem isn't fixing it.
- Platform: measure, monitor, benchmark, attribute.
- Consultancy: reveal, prioritise, and execute (content, PR, listings, knowledge signals, IA/schema) with the right people guiding you.
- Operating rhythm: learnings, experiments, briefs that move scores.
Outcome: clarity + action in one engagement.

Question 5

Why is Zebora the leader in AI brand measurement?

Accepted Answer

We unite a proprietary measurement platform with hands-on growth expertise.
- First to align visibility, recommendation, competitive position, and perception into one scorecard.
- Rigorous taxonomy, scoring, weighting, and framing rules; custom fit to each brand/category.
- Action frameworks: Information Authority Audit, Knowledge Signal Uplift, Narrative Gap Close, LLM Content Briefs.
- Team with 15+ years in marketing & data; category-defining launches behind us.
Outcome: research-grade measurement, practical playbooks, measurable lift.

Question 6

Which AI models does Zebora track?

Accepted Answer

Primary coverage today is ChatGPT (English; UK/US/AUS focus), with capability to expand into Claude, Gemini, and Perplexity.
- Depth where usage is strongest now; expansion as other models mature.
- Country/language nuances respected; any English-language market can be tested.
Outcome: coverage where it matters now, with a clear roadmap.

Question 7

What signals do AI models use to form opinions about brands?

Accepted Answer

Models synthesise multiple sources and weigh authority, trust, clarity, and relevance. We capture and categorise what they cite.
- Owned: website, docs, product pages, blog, about/info.
- Third-party: Wikipedia/Wikidata, reviews, price comparison, forums, news/analyst coverage, blogs, social, etc.
- Framing factors: recency, coverage depth, consensus, contradiction.
Zebora records cited URLs, classifies them, and links them to score movements.
Outcome: you know which signals to strengthen to shift answers.

Question 8

What does Zebora measure inside AI models?

Accepted Answer

Five pillars:
- Visibility (BVI): how often/where your brand appears across high-intent prompts and geographies.
- Recommendation (LRS): 1–5 sentiment on whether models recommend you (incl. first-person and relevance prompts).
- Competitive Position Index: how you rank vs. competitors across products, features, and use cases.
- Brand perception: Authority, Trustworthiness, Clarity + your own brand values, and whether products are accurately represented.
- Signals: which references are cited (owned vs. third-party; type mix) and how they influence scores.
All scores are benchmarked over time.
Outcome: a clear scorecard with drivers to improve.

Question 9

How does Zebora measure brand presence inside AI models like ChatGPT and Claude?

Accepted Answer

We run a structured prompt set that mirrors real behavior at scale, score outcomes, and benchmark competitors.
- Prompt taxonomy: master category, use case, audience, product type/feature, USP/differentiators, competitor matchups, brand-attribute prompts.
- Scorecards: BVI (appearance/ordering), LRS (1–5 recommendation sentiment with weighting), Perception (five attributes, 1–5 with half-points and framing rules).
- Weighted answers to reflect real-world impact.
- Logs of references/URLs cited to understand driving signals.
- Re-measurement: repeated runs with movement analysis.
Outcome: a quantified view of where you win/lose—and why.

Question 10

How is Zebora different from SEO or traditional brand tracking tools?

Accepted Answer

SEO optimises for search engines; Zebora optimises how your brand is shown and recommended inside LLM answers.
- Measures in-answer visibility/order and recommendation sentiment, not keyword rankings.
- Maps and closes gaps in sources LLMs cite (official site, Wikipedia, reviews, press, docs).
- Tracks brand perception attributes alongside visibility and recommendations.
- Converts insights into content, PR, listings, and knowledge-signal work.
Outcome: stronger AI recommendations, visibility, and growth.

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