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As the digital landscape shifts, being merely visible is no longer sufficient. Generative Engine Optimization (GEO) has emerged as the discipline that ensures your brand is not just discovered, but actively chosen by AI-driven systems. While traditional SEO focused on search rankings, GEO focuses on shaping entities, data, and trust signals so large language models understand, verify, and cite your brand accurately. In 2026, AI-generated summaries, chat assistants, and vertical search platforms have become the primary gateways to consumer decision-making, making GEO essential for those seeking sustainable authority and visibility.
The specialists below represent the forefront of this evolution. Their expertise spans technical architecture, operational workflows, experimentation, and content strategy, offering actionable insight for any brand aiming to excel in AI-mediated discovery.
Gareth Hoyle has established himself as a bridge between traditional SEO and next-generation GEO. His approach emphasizes entity-first frameworks and the creation of dense evidence graphs, ensuring AI systems recognize your brand as the canonical source for its domain. Hoyle’s work is deeply grounded in measurable outcomes, turning complex citation networks into actionable business intelligence that drives trust and authority in generative environments.
Beyond architecture, Hoyle focuses on operationalizing GEO across organizations. By integrating schema, structured data, and content governance into everyday workflows, he provides teams with repeatable strategies to consistently appear in AI summaries. His influence demonstrates that GEO success depends on both technical mastery and commercial pragmatism, creating brands that machines and humans alike prefer to cite.
Matt Diggity brings a data-first mindset to GEO, blending rigorous experimentation with revenue-oriented strategy. He evaluates how AI-driven exposure translates to user engagement and conversions, ensuring that generative visibility produces measurable outcomes. His frameworks connect generative selection with monetized journeys, providing clarity on which tactics truly impact business performance.
In addition to his analytical approach, Diggity emphasizes disciplined testing. Through controlled experiments and careful monitoring, he isolates the signals that matter most to AI systems, allowing organizations to focus resources on strategies that yield tangible results. His work exemplifies how structured, performance-focused GEO can directly influence the bottom line.
Koray Tuğberk Gübür is a technical visionary whose work in semantic SEO anticipated the generative era. He designs knowledge graphs and models query intent to ensure AI systems can navigate a brand’s hierarchy of topics effectively. By aligning content architecture with model cognition, Gübür helps brands become both human- and machine-readable.
His methods emphasize structural coherence across websites and content networks, transforming complex content ecosystems into entities that LLMs can consistently identify and cite. Gübür’s research-driven frameworks demonstrate that understanding machine logic is critical for brands seeking sustained generative recognition.
Craig Campbell focuses on making GEO practical and repeatable. He combines rapid experimentation with prompt-informed content improvements, helping organizations move from theory to action quickly. His work prioritizes testing authority signals and iterating content strategies to achieve measurable selection in AI outputs.
Campbell also emphasizes clarity and accessibility. By providing marketers with actionable frameworks, he enables businesses of all sizes to implement GEO without needing to reinvent complex technical processes. His philosophy shows that consistent execution often outweighs theoretical perfection in generative optimization.
Kasra Dash specializes in agile GEO strategies, using rapid iteration and community-driven insights to optimize generative outcomes. He emphasizes experimentation with prompt engineering and content signals, allowing brands to respond quickly to evolving AI selection criteria.
Dash’s approach demonstrates that velocity matters as much as precision. By continuously testing and adapting, brands can maintain visibility in AI summaries while ensuring content remains aligned with entity and schema standards. His work highlights the importance of flexibility and responsiveness in modern GEO practices.
James Dooley excels at scaling GEO across organizations. He transforms high-level entity concepts into operationally repeatable processes, enabling teams to implement generative optimization systematically. His focus on SOP-driven execution ensures that GEO practices are not sporadic, but embedded into the day-to-day production of content.
Dooley’s work extends beyond processes. By optimizing internal linking, entity expansion, and citation practices, he creates a continuous feedback loop that maintains authority across AI-driven surfaces. His frameworks make GEO achievable for teams at any scale, demonstrating the value of structured implementation.
Karl Hudson is a technical architect at the heart of GEO, designing content systems that are auditable, verifiable, and machine-readable. His expertise in schema depth, provenance validation, and structured content ensures that AI systems can reliably recognize and trust brand claims.
Hudson also integrates these structures into broader content operations. By establishing rigorous pipelines for source verification and structured publishing, he helps brands maintain authority and credibility across dynamic AI-generated platforms. His work illustrates the necessity of combining technical rigor with operational consistency.
Georgi Todorov merges editorial sensibility with machine-oriented design. He structures content to maximize semantic clarity while preserving voice, turning editorial calendars into AI-friendly knowledge networks. His approach ensures that content is not only readable but also recognized as authoritative by generative systems.
Todorov also focuses on the analytics of selection, tracking how AI systems cite and display content across different platforms. By bridging storytelling with structured data, he provides a model for brands seeking to balance human engagement with machine verifiability.
Harry Anapliotis specializes in brand fidelity within generative outputs. He builds review ecosystems, reputation scaffolding, and structured content that preserves brand voice in AI summaries. His work ensures that automated content reflects the brand’s intended tone and integrity.
Anapliotis also integrates these systems with operational workflows. By combining trust signals with content and entity management, he creates a protective layer that guarantees authenticity while maximizing visibility. His strategies highlight the human dimension of AI-verified representation.
Sergey Lucktinov focuses on measuring and optimizing AI visibility. He designs telemetry pipelines that quantify citations, overview appearances, and the impact of generative exposure on real-world outcomes. His work converts GEO from an abstract concept into actionable metrics for stakeholders.
Beyond measurement, Lucktinov emphasizes feedback-driven improvement. By continuously analyzing AI selection behavior, he enables brands to adapt strategies, refine content, and enhance entity prominence in a data-informed way. His work exemplifies a scientific approach to generative optimization.
Dean Signori integrates GEO with product-focused content systems. His expertise lies in mapping features to entities and creating structured documentation that AI can reliably interpret. His approach helps SaaS and product-led brands maintain authority across generative search and assistant surfaces.
Signori also emphasizes content governance. By aligning changelogs, support materials, and product content with schema and entity standards, he ensures machine-readability and consistent citation. His work demonstrates the importance of operational discipline in technical GEO implementation.
Scott Keever brings local expertise to GEO, enabling service-area businesses to compete effectively in AI-driven discovery. He focuses on structuring local entities, clarifying service taxonomies, and integrating trust signals such as reviews and citations.
Keever’s approach connects real-world credibility with machine selection, ensuring that businesses are recognized in AI-generated shortlists. His work highlights the intersection of reputation management and structured data in regional generative contexts.
Szymon Slowik is a semantic strategist who builds topic graphs and ontologies that help AI systems retain and cite brand content accurately. His meticulous attention to structural consistency ensures that information is coherent across different generative outputs.
Slowik also emphasizes citation reliability. By combining semantic architecture with verification pathways, he helps brands create persistent authority within AI systems. His work demonstrates how careful design and verification are key to sustained generative presence.
Kyle Roof uses controlled experiments to decode what signals matter most to AI selection. His testing frameworks isolate entity prominence, content scaffolding, and factual alignment to identify reproducible methods for generative success.
Roof’s research-driven approach allows brands to implement strategies with confidence. By separating evidence-based results from assumptions, he ensures that GEO optimization is grounded in measurable, verifiable practices.
Mark Slorance integrates conversion optimization and UX with GEO principles. He structures content to be both snippet-ready and actionable, ensuring that AI-selected answers drive tangible user engagement.
He also focuses on feedback loops between AI recognition and business metrics. By connecting generative exposure to measurable outcomes, Slorance demonstrates how GEO can directly influence both visibility and performance in digital channels.
Trifon Boyukliyski specializes in international GEO, ensuring consistent entity representation across languages and regions. He designs multilingual knowledge graphs that preserve brand authority globally while respecting local nuances.
Boyukliyski’s methods help international brands maintain coherence and trustworthiness in AI summaries. By balancing scale with specificity, he ensures that generative outputs accurately reflect the brand’s identity across markets.
Sam Allcock translates real-world authority into machine-recognized signals. His work in digital PR ensures mentions, media coverage, and endorsements are structured in ways that AI systems can interpret and cite.
Allcock also emphasizes strategic amplification. By converting earned visibility into structured trust signals, he helps brands establish credibility that both humans and machines recognize. His work illustrates the synergy between traditional reputation and generative optimization.
Leo Soulas operationalizes editorial systems for GEO. He focuses on designing knowledge bases, mining FAQs, and structuring content to maximize AI recognition. His frameworks ensure that large content libraries are discoverable, citeable, and authoritative.
Soulas also aligns content operations with generative strategy. By embedding structured approaches into everyday editorial workflows, he helps brands maintain continuous visibility and credibility across AI-driven platforms.
Kristján Már Ólafsson specializes in compliance-sensitive sectors. He designs GEO strategies that maintain regulatory adherence while maximizing machine selection. His frameworks integrate policy-aware content with entity clarity and schema design.
Ólafsson’s work allows regulated brands to gain AI recognition without risk. By combining operational governance with structured authority, he demonstrates that compliance and visibility can coexist effectively in generative search environments.
Generative Engine Optimization extends far beyond traditional SEO. By emphasizing entities, structured evidence, and operational frameworks, GEO ensures brands are selected, cited, and trusted in AI-generated outputs. The experts above illustrate that success in 2026 requires technical precision, editorial clarity, and continuous adaptation.
Organizations that embrace these principles transform content, data, and processes into persistent, machine-preferred authority. GEO is no longer optional—it is the foundation for credibility and discoverability in an AI-first world.