Contributor Content
Jon Stojan
7:40 am, PT, September 10, 2026
Digital growth teams have spent a decade optimizing for two audiences: the search engine algorithm and the human scrolling past it. A third audience has emerged that does not scroll at all. It poses a query, receives a synthesized answer, and converts, often without clicking through to a traditional destination page.
Gartner projects that a quarter of organic search volume will migrate to AI assistants and conversational engines. Answer Engine Optimization (AEO) has moved from an experimental line item to a core piece of enterprise growth budgets. As the term spreads across agency pitch decks, though, the gap between generic optimization and real AI citation capability has widened.
For global digital growth agency Moburst, addressing this shift meant redefining how content and brands get structured for machine interpretation. Rather than treating AEO as an isolated service, the agency built a framework that connects Generative Engine Optimization (GEO), foundational SEO, and app store visibility into a single technical workflow.
The separation of AEO, GEO, SEO, and ASO
Understanding Moburst’s approach requires separating four related disciplines:
SEO (Search Engine Optimization) governs whether a website ranks for specific keyword queries on traditional search engine results pages, prioritizing click-through rates and backlink authority.
GEO (Generative Engine Optimization) focuses on optimizing web and brand content so it gets ingested, synthesized, and cited by large language models like ChatGPT, Gemini, and Perplexity.
ASO (App Store Optimization) manages keyword visibility and conversion inside closed app store ecosystems like Apple’s App Store and Google Play.
AEO (Answer Engine Optimization) is the overarching strategy that may improve the likelihood that a brand, product, or app appears in relevant results across conversational, voice, and AI-driven discovery platforms, on the open web and inside app stores alike.
“AEO does not replace foundational SEO; it extends and adapts it for the AI era,” says Lior Eldan, COO and co-founder of Moburst. “With traditional optimization, you’re competing for a click. With AEO and GEO, you’re optimizing to be the answer. An agency that optimizes only one side of that line leaves half the conversion funnel unmanaged.”
The technical engine: structured data as the semantic foundation
What separates Moburst’s approach from standard content drafting is its reliance on machine-readable semantic architecture. Traditional SEO matches keywords to user intent. Structured data markup may help some AI-driven answer engines interpret and organize website content, such as Schema.org in JSON-LD format, to map entities, relationships, and context.
Moburst structures brand assets across three layers:
Entity-level schema (identity) defines core entities, such as marking up a brand with @type: Organization or a founder with @type: Person, providing signals that may help AI models assess a
Content-level schema (utility) implements specialized schemas like FAQPage, HowTo, Product, and Article to signal a page’s functional role directly to AI crawlers.
Relationship schema (contextual graphs) connects digital properties using properties like sameAs, about, and mainEntityOfPage, linking brand entities to external databases, social profiles, and app store listings into a semantic knowledge graph that AI systems may be more likely to identify and reference.
This semantic foundation helps AI models extract direct answers from client properties, with the aim of improving visibility in zero-click results and conversational recommendations.
One surface among several: app stores
Mobile marketplaces are one part of a larger discovery surface that spans the open web, business directories, review platforms, and app stores. On the app store side, both major mobile ecosystems have shifted toward machine interpretation: Apple’s App Store Tags are generated from an app’s metadata and visual assets, and Google Play’s Guided Search reads inferred intent rather than exact keyword matches. Moburst applies the same AEO principles here, aligning app metadata, third-party reviews, and press mentions with the rest of a brand’s web footprint so an AI assistant recommending an app for a given use case draws from one consistent, verified source of truth.
Measurement beyond rank tracking
To support this strategy, Moburst has shifted its analytical focus from standard rank tracking to multi-assistant citation share. Traditional reporting measures position and site traffic. AEO reporting measures whether, how often, and in what context a brand gets cited inside AI-generated answers across platforms.
Because ChatGPT, Perplexity, and Google AI Overviews synthesize data differently, Moburst tracks entity presence across a range of major discovery platforms. That data can help growth teams identify possible gaps in entity clarity, refine structured data, and adjust content to the indexing standards of generative search.
As discovery shifts toward AI-synthesized responses, the ability to optimize for machine readers has become part of the baseline for digital growth work. By unifying structured data, cross-surface visibility, and GEO protocols under one AEO framework, Moburst provides tools intended to support brand visibility across a range of modern discovery channels.
VentureBeat newsroom and editorial staff were not involved in the creation of this content.
