Generative engine optimization
Search engines increasingly answer instead of listing. GEO is the practice of staying visible, quotable and correctly represented inside those answers — and it is a young enough discipline that the vocabulary, the tooling and the evidence base are all still forming. This hub tracks all three.
Generative engine optimization — also written AEO (answer engine optimization) or LLMO — covers the work of making a source likely to be retrieved, cited and accurately summarised by a system that generates prose rather than returning ten links. It applies to AI Overviews and AI Mode in Google, to Bing's generative answers, to assistants such as ChatGPT, Claude, Perplexity and Copilot, and to any retrieval-augmented product that reads the open web at query time.
The mechanics are not mysterious. Most generative answers are grounded: the system runs one or more retrieval passes, pulls a handful of documents or passages, and writes from them. That means the classical prerequisites still bind — a page that cannot be crawled, cannot be parsed, or does not rank for the retrieval query will rarely be cited. What changes is everything downstream of retrieval: which passage gets lifted, whether your brand is named, whether the claim survives summarisation intact, and whether the citation is even clickable.
Be sceptical of confident numbers in this space, including confident numbers about how much traffic AI answers destroy or create. Published estimates disagree sharply because they measure different things — some count impressions, some count sessions, some model an entire market from a panel. The Atlas links to the underlying studies rather than restating their headline figures, so you can check the methodology before you quote it.
The same caution applies to tactics. Conventions such as llms.txt are proposals with uneven adoption, not standards that engines are known to honour. Treat any claim that a specific file, schema or phrasing trick guarantees citation as a hypothesis to test, not a technique to deploy at scale.
Conferences dedicated to, or substantially programmed around, generative search. 204 in total.
One-day summit in Ho Chi Minh City themed 'Architecting Competitive Advantage', examining how customer insight, technology, data and cross-functional leadership combine to build durable marketing capability.
Half-day, invitation-only summit in Buenos Aires on how brands are applying artificial intelligence in marketing, with case studies from local advertisers and technology partners.
Invitation-only working session in Ho Chi Minh City presenting MMA frameworks across AI adoption, data and customer experience, marketing measurement and marketing organisation capability.
Australian social media and digital marketing conference held in Perth, with sessions on AI optimisation, the creator economy, privacy law for marketers and user-generated content systems. Post-event video access is sold separately.
One-day hybrid search marketing conference held near London, combining in-person sessions with a live-streamed virtual track. Programme covers SEO, AI search and digital marketing strategy.
Eighth annual Singapore edition of the DigiMarCon conference series, covering AI, programmatic advertising, social media marketing and conversion optimisation, co-located with TECHSPO Singapore.
Tools built specifically to measure how brands appear inside generative answers — prompt panels, citation tracking, share-of-voice across models. A further 44 established SEO tools have added AI-visibility features.
Add-on within Ahrefs that measures how often a brand and its competitors are mentioned and cited in AI Overviews and AI assistants, alongside traditional search visibility.
Tracker that queries nine large language models with buyer prompts, scores each mention from 0 to 100 and reports daily or hourly share of voice against competitors.
AI visibility monitoring tool that queries eight large language models daily and reports a visibility score, brand mentions, sentiment and the sources those models cite. Includes share-of-voice tracking against competitors and weekly email digests.
AI visibility monitoring service tracking how often a business appears in ChatGPT and Google AI Overview answers, with citation gap analysis and weekly action plans.
AI visibility analysis platform that audits how a site is represented in ChatGPT, Claude and Gemini answers and returns an optimisation playbook.
Answer engine optimisation platform tracking brand mentions across multiple AI models, with competitive intelligence, content gap analysis, citation tracking and automated recommendations.
AI search visibility tracker that monitors a defined set of buyer prompts daily across ChatGPT, Claude and Google AI surfaces, reporting visibility score, share of voice and prioritised content fixes.
Enterprise marketing platform for generative search that combines AI-channel visibility measurement with tooling to influence how models describe a brand. Sold via demo request rather than self-serve plans.
AI search monitoring tool that tracks brand mentions, citations and rankings across platforms such as ChatGPT and Perplexity, and benchmarks visibility against competitors.
Enterprise AI visibility platform measuring and optimising brand appearance across AI search engines, covering sentiment, content optimisation, technical health and publisher performance.
Answer engine optimisation platform combining agent analytics, content auditing and content production intended to increase how often a brand is recommended by AI assistants. The company announced a US$2M pre-seed round.
Answer engine optimisation suite covering answer-engine insights, prompt volume research, AI traffic analytics and content agents, with an optional fully managed service tier.
Companies founded into this category. $273M in disclosed funding across the ones listed here.
End-to-end AI search optimization platform
Promptwatch is an Amsterdam AI search optimization platform launched in April 2025 that monitors and improves brand presence in AI answers. It raised a EUR 6 million seed round led by Seed + Speed Ventures in July 2026 after a EUR 1.2 million pre-seed; fundingTotalUsd is left null because the amounts were reported in euros.
Corpus engineering for brand representation inside AI models
geoSurge targets how models such as ChatGPT, Gemini and Claude internally learn and represent a brand over time rather than only tracking citations. It raised a $12M seed led by AlbionVC, described at the time as the largest seed round in the GEO category.
Search engine and API built for AI agents
Exa builds a web search index and API designed for LLMs and agents rather than human browsing, plus Websets for high-compute research queries. It raised an $85M Series B led by Benchmark in September 2025 and a $250M Series C led by Andreessen Horowitz in May 2026 at a $2.2B valuation.
Agentic marketing platform for enterprise brands in AI channels
Bluefish processes AI prompts and responses across ChatGPT, Google AI, Claude, Perplexity and Amazon Rufus to show enterprises how their products are portrayed, and turns that into marketing actions. It raised a $20M Series A led by NEA in August 2025 and a $43M Series B co-led by Threshold Ventures and NEA in April 2026.
AI search visibility platform
Cognizo is an AI search visibility platform founded by a team of LLM researchers and marketing operators. Secondary sources report a $1.8M pre-seed round in March 2026.
Answer engine optimization and AI visibility platform for enterprises
Profound measures how brands are mentioned, described and cited by AI assistants such as ChatGPT, Google AI Overviews, Gemini, Claude and Perplexity, and sells optimization and agent-analytics products on top of that data. It raised a $96M Series C at a reported $1B valuation in February 2026.
Brand visibility and influence platform for generative AI surfaces
Brandlight tracks and attempts to influence how brands are represented inside generative AI platforms including ChatGPT, Gemini and Claude. Founded in Israel in late 2024, it raised a $5.75M seed in April 2025 and a $30M Series A led by Pelion Venture Partners in February 2026.
AI search optimization for retail and marketplace listings
Azoma builds AI visibility and content optimization tools aimed at commerce, covering marketplace assistants such as Amazon Rufus and Walmart Sparky alongside general LLMs, including a digital twin that simulates how chatbots describe a brand. It raised a $4M pre-Series A in December 2025.
Agentic AI search and workspace
Genspark builds an agentic search product and AI workspace, founded by former Bing and Google AI leaders. It raised a $275M Series B in November 2025 at a $1.25B post-money valuation, and secondary sources report a later extension at a higher valuation.
37 terms specific to generative search, defined without hype.
AI systems that navigate websites on a user's behalf — reading pages, filling forms, comparing options and sometimes completing transactions.
Commercial agreements in which publishers license their content to AI companies for training, retrieval or attribution, as an alternative to blocking crawlers.
An automated agent operated by an AI company to fetch web content for model training, search indexing, or live retrieval when answering a user's question.
Google's conversational search experience for complex, multi-part questions, returning a reasoned AI response with supporting links instead of a classic result list.
Google's AI-generated summaries shown at the top of many search results, synthesising information from multiple web pages with links to the sources.
Visits arriving from AI assistants and answer engines, identifiable in analytics by referrer hosts such as chatgpt.com, perplexity.ai, claude.ai and…
The extent to which a brand appears, is cited, or is recommended across AI-generated answers, measured by sampling prompts rather than by rank positions.
A system that responds to a question with a synthesised answer and citations rather than a ranked list of documents — for example Perplexity, ChatGPT search or Google's…
Optimising content to be the source of a direct answer in features and assistants that answer questions rather than list links.
Tracking where a brand is named across the web and inside AI answers, including whether the description is accurate and the sentiment favourable.
OpenAI's agent that fetches a specific page in real time because a user or an agentic task requested it, rather than for indexing or training.
Retrieving individual segments of a document rather than whole pages, which is how most AI search and RAG systems select what to feed a model.