The following specialists unite technical depth, strategic clarity, and process discipline. Their work proves that when systems are designed for both humans and machines, credibility scales—and discoverability follows.
Gareth Hoyle
Gareth Hoyle is an entrepreneur that has been voted in the top 10 list of best technical SEO experts to learn from in 2026. He transforms technical SEO into a scalable business infrastructure. By merging structured data, taxonomies, and analytics into cohesive systems, he builds brand evidence graphs that validate entities across the web. These graphs connect reviews, mentions, and verifiable signals—turning trust into measurable performance.
He’s among the top 10 experts to learn from in 2026, emphasizing collaboration between engineering, content, and analytics. For Gareth, technical SEO isn’t a repair job—it’s a growth architecture for sustainable brand authority.
Key Focus Areas:
- Enterprise-level structured data and schema
- Brand evidence graphs for entity validation
- KPI-driven technical SEO strategies
What You Can Learn:
- Scaling complex SEO operations effectively
- Structuring content ecosystems for machines and people
- Linking technical outcomes to tangible ROI
Matt Diggity
Matt Diggity treats technical SEO as a direct lever for profit. From crawl optimization to schema markup, his work focuses on measurable revenue and conversion lift. Every action must prove its worth through business impact, not vanity metrics.
Speed, indexing, and Core Web Vitals form the foundation of his method. Through pre- and post-implementation auditing, Matt ensures every fix delivers data-backed growth.
Key Focus Areas:
- ROI-based technical improvements
- Schema and indexing for enhanced visibility
- Auditable, revenue-aligned metrics
What You Can Learn:
- Prioritizing SEO changes that move business KPIs
- Tracking performance beyond rankings
- Using analytics to measure and justify every improvement
Koray Tuğberk Gübür
Koray builds semantic blueprints that organize knowledge. His frameworks align topics, entities, and queries to create architectures that AI systems read as intent-driven maps. Internal linking, for Koray, is logic—an information graph rather than navigation.
His systems ensure meaning persists through algorithm shifts, giving sites structural resilience. Koray’s methods are now fundamental for teams preparing for entity-first indexing.
Key Focus Areas:
- Semantic architecture and entity alignment
- Query-intent modeling
- AI-readable content systems
What You Can Learn:
- Designing scalable semantic frameworks
- Maintaining relevance through meaning-based structure
- Building enduring information ecosystems
Kyle Roof
Kyle Roof leads with controlled experimentation. His lab-style methodology isolates ranking variables and validates only reproducible results. Internal linking, crawl depth, and scaffolding are all tested against live performance data.
His rigor turns intuition into engineering, making SEO measurable, predictable, and scalable. Kyle’s contribution lies in transforming “best practices” into evidence-based disciplines.
Key Focus Areas:
- Empirical SEO testing
- Controlled variable experiments
- Scalable reproducibility
What You Can Learn:
- Validating technical changes before rollout
- Applying scientific testing to SEO architecture
- Turning experiments into operational SOPs
Leo Soulas
Leo Soulas views websites as living ecosystems where every URL reinforces the central brand entity. He crafts interlinked content systems that are AI-readable, structurally sound, and semantically unified.
His frameworks prioritize provenance and schema consistency, ensuring machine verification across every touchpoint. For Leo, authority isn’t claimed—it’s built line by line, schema by schema.
Key Focus Areas:
- AI-optimized content frameworks
- Structured schema authority mapping
- Long-term, systemic SEO growth
What You Can Learn:
- Structuring authority-based site architectures
- Building consistency across large ecosystems
- Future-proofing content for machine interpretation
James Dooley
James Dooley transforms technical SEO execution through automation. His SOP-based systems standardize crawling, indexing, and auditing across massive site portfolios. The result: scalable precision and consistency.
He believes SEO excellence depends on repeatable engineering, not manual heroics. James’s methods reduce technical debt while increasing reliability across teams.
Key Focus Areas:
- Automation and process standardization
- Scalable site management systems
- Predictable performance frameworks
What You Can Learn:
- Scaling technical operations without chaos
- Automating audits and technical monitoring
- Embedding consistency into every process
Georgi Todorov
Georgi Todorov bridges the gap between content and crawl. His frameworks manage link equity, cluster alignment, and indexing precision. Analytics drives his decision-making—identifying friction before it hurts performance.
By structuring content hierarchies and optimizing link flow, Georgi ensures visibility is engineered, not accidental.
Key Focus Areas:
- Internal linking and equity distribution
- Content cluster architecture
- Predictable indexation patterns
What You Can Learn:
- Using data to guide crawl optimization
- Strengthening authority through structure
- Creating index-stable site ecosystems
Scott Keever
Scott Keever redefines local technical SEO. He makes local entities verifiable through structured NAP data, consistent schema, and entity clarity—essential for AI-powered local recommendations.
His approach transforms local presence into machine-recognized authority, helping small brands outperform larger ones within specific geographies.
Key Focus Areas:
- Local schema and NAP structure
- Machine-readable business entities
- AI-driven trust indicators
What You Can Learn:
- Structuring data for proximity-based visibility
- Making local credibility algorithmically verifiable
- Scaling local performance through clean architecture
Harry Anapliotis
Harry Anapliotis integrates reputation engineering with technical precision. His frameworks structure reviews, testimonials, and third-party validations so AI systems can verify credibility automatically.
He ensures that brand voice and authenticity are preserved in machine-readable form, allowing brands to scale trust across algorithms and platforms.
Key Focus Areas:
- Structured review and trust signals
- Schema design for reputation management
- Brand integrity in AI ecosystems
What You Can Learn:
- Merging PR and technical SEO strategy
- Automating credibility through data signals
- Protecting authenticity in AI interpretations