AI Visibility Knowledge Centre
A practical guide to building evidence for B2B brands across ChatGPT, Perplexity, Claude and Google Gemini.
Operational summary
AiVisible's practical library: definitions, industry scenarios, measurement methodology and AI Visibility research protocols.
Start with the knowledge map, then move to an industry scenario, definition, methodology or research protocol.
Assess whether your category is suitable for AI Visibility measurement.
Start with a free category-fit assessment. We will confirm whether a Snapshot, an audit or work on another foundation is the right next step.
Map of concepts and procedures (BLUF)
To understand AI Visibility and move from concepts to decisions, follow this sequence:
- 1Start with definitions in the Glossary to separate useful concepts from myths.
- 2Understand measurement in the Methodology so progress can be reported responsibly.
- 3Review the research protocols and rules for documenting results.
- 4Finally, review pricing to choose the right engagement format.
Our quality standard
Explicit definitions, measurement protocols and interpretation limits.
Results published with the sample, date, method and limitations.
Every service documented with transparent scope boundaries.
Knowledge collections
AI Visibility scenarios for categories where buying is expensive, risky or depends on expert trust.
Clear definitions of the concepts used to measure and improve brand visibility in AI answers.
How we measure AI Visibility using question sets, answer evidence, sources and repeatable comparison.
Measurement methods, protocols and evidence standards used in AiVisible research.
Available latest resources
AI Visibility for Law Firms
Law firms are often held back by generic service descriptions and a lack of specialisation pages that AI can cite for specific legal matters.
AI Visibility for Clinics and Medical Specialists
In healthcare, AI looks for evidence of safety, qualifications and scope of care. An attractive website alone is not enough.
AI Visibility for SaaS and B2B Companies
SaaS brands win visibility through comparisons, use-case pages and clear problem positioning—not through a product landing page alone.
AI Visibility for Real Estate and Construction
AI needs evidence of completed work, project specifications and delivery credibility in this sector—not just an image gallery.
AI Visibility for Training and Expert Services
Expert brands are often omitted when they do not publish course programmes, trainer credentials and practical use cases.
AI Visibility for E-commerce
E-commerce brands lose visibility when they have good product descriptions but no advisory, comparison or expert content that AI can use in buyer answers.
AI Visibility for Marketing Agencies
Agencies lose enquiries when they claim to do everything. AI is more likely to surface firms with a clear specialisation and documented results.
AI Visibility for HR and Recruitment Firms
HR firms rarely appear when they lack evidence of sector expertise, selection methodology and documented outcomes.
AI Visibility for Financial Advisers and Finance Firms
Finance has an exceptionally high credibility threshold. Documented qualifications and transparent service boundaries help models evaluate a provider safely.
AI Visibility for Software Companies and IT Firms
A generic full-stack positioning is hard for AI to match. Clear technical and sector expertise, backed by delivery evidence, is easier to recommend.
AI Visibility for Manufacturing and B2B Industry
Manufacturers are overlooked when websites rely on claims about tradition and quality instead of specifications, certifications, minimum order quantities and lead times.
Generative Engine Optimization (GEO)
GEO does not replace SEO. It adds a layer of clarity and citability for AI systems that generate answers.
AI Overviews
In the AI Overviews era, organic position alone is not enough. Content also needs to work as evidence for an answer.
IndexNow in GEO and AI Search
IndexNow can accelerate discovery of changes, but it does not guarantee citation or recommendation by an AI system.
Entity Grounding and E-E-A-T in LLMs
Without consistent entities and E-E-A-T signals, AI systems are more likely to rely on large directories or aggregators than a company website.
Content Citability for LLMs
Citability improves when each section answers a question and provides context or evidence instead of relying on a sales slogan.
Brand Gravity
Brand Gravity grows when AI treats your brand as credible recommendation evidence—not merely as a name it can mention.
Share of Model
Share of Model is an AI-era counterpart to share of voice: it measures how often a model recommends your brand relative to competitors.
AEO — Answer Engine Optimization
AEO optimises for systems that answer instead of only linking, including ChatGPT, Perplexity and Google AI Overviews.
Brand Entity
A brand entity is the structured understanding an AI system has of your company. Stronger grounding supports more accurate matching and recommendations.
AI Visibility Audit Methodology
This methodology explains how to establish a baseline, assess AI answers and compare results after implementation.
Control Question Set for AI Search Measurement
A useful question set reflects how people actually ask for help—not just branded and marketing keywords.
AI Visibility-ready Publishing Checklist
This checklist improves QA and prevents visually polished pages from being published with weak structure for crawlers and AI systems.
The knowledge centre is only a starting point
A structured audit reveals where AI systems lack the evidence needed to describe and recommend your brand accurately.
0 PLN · Fit assessment, without a promise of a full report