Publications
DiscoverabilityHQ publishes foundational research, frameworks and sector studies on how organisations become understood, trusted, compared and recommended by AI.
Research library
The library separates category-defining work from reusable frameworks and sector-specific research so readers can move from thesis to method to market application.
Foundational Research
These publications define the category and explain why discoverability is moving from search visibility to recommendation confidence.
The category-defining guide to AI Discoverability, GEO, AEO, LLM optimisation, AI visibility and the work of becoming understood, trusted and recommended by AI systems.
A flagship essay on why search visibility is becoming recommendation visibility, and what that shift means for commercial strategy, evidence and authority.
Frameworks
The framework layer gives leadership teams a shared diagnostic language for AI-mediated discovery.
A clear model for how organisations become understood, trusted, compared and recommended by AI: Understand, Believe, Evaluate and Recommend.
Sector Research
Sector editions apply the Discovery Model™ to specific evidence environments. Forthcoming reports now combine market state-of-play review, AI-answer visibility, search-interest data, crawl/index evidence, source records and sector-specific public evidence.
A baseline research edition applying the Discovery Model™ to luxury fashion and leather goods, with a prompt taxonomy, evidence map, measurement approach and explicit limits before benchmark claims are made.
Research question: How and why do AI systems understand, trust, compare and recommend luxury fashion and leather-goods brands in context?
A forthcoming research brief on hotels, hospitality and contextual recommendation, combining AI-answer visibility, search-interest signals, source records, travel editorial and booking evidence.
Research question: How do AI systems decide which hotels, hospitality brands and destination options fit a traveller's context?
A forthcoming research brief on consumer purchase recommendation, brand visibility and category visibility, joining prompt data, search demand, product feeds, reviews and buying-guide evidence.
Research question: How do AI systems surface retailers, brands and categories when users ask what to buy, where to buy it and why?
A forthcoming research brief on high-trust, high-consequence recommendation behaviour, combining AI-answer data, search demand, regulators, provider disclosures and consumer-finance sources. It is not financial advice.
Research question: How do AI systems handle trust, evidence and caveats when users ask about personal finance providers, products and information sources?
A forthcoming research brief on high-trust, high-consequence healthcare discoverability, bringing together AI source records, search-interest context, public health sources, professional registers and provider evidence.
Research question: How do AI systems understand and surface healthcare organisations, providers and information sources without crossing into medical advice?
Publication standards
Published reports, baseline editions and forthcoming research briefs are labelled differently so readers can see what has been measured.
The library avoids rankings, scores, percentages and platform claims where the supporting prompt-run dataset is not present.
Each edition defines the commercial context, likely evidence families, prompt classes, risks and limitations before findings are reported.
The central question is not simply whether an organisation appears, but whether it can be understood, trusted, compared and responsibly recommended.
Editorial approach
DiscoverabilityHQ publications translate fast-moving developments in AI, search, consumer behaviour and commercial discovery into practical strategic language while keeping measured evidence, interpretation and hypotheses visibly separate.
Executive briefing
Request a focused briefing on how AI systems may understand your category, your competitors and the signals shaping recommendation.