About
The throughline behind 8+ years of search work.
Make complex search problems clearer, more measurable, and more actionable.
The terminology I work under has changed several times: SEO, technical SEO, information architecture, entity strategy, AI search. The underlying work has not. I investigate how search systems understand information, then turn what I learn into clearer architectures, practical strategies, and repeatable workflows.
How the work evolved
Six stages, one recurring question: can this information be understood?
- 01
Client SEO and Local SEO
I began working in SEO in 2018 as an SEO Strategist and Account Lead through KSullivan Consulting LLC, managing client engagements from technical audits and keyword research through on-page implementation, Local SEO, content planning, link building, reporting, and client strategy.
- 02
Technical SEO and multi-location strategy
Technical audits, site architecture, and multi-location search, where visibility problems are usually structural rather than editorial.
Ideal Turf — integrated Local, technical, content, and architecture strategy
- 03
Information architecture and content consolidation
As sites grew larger, the recurring bottleneck stopped being content volume and became how information was organized, connected, and found.
Heavenly Greens — consolidated more than 1,000 pages into approximately 100 intentional assets
- 04
Entity strategy
That focus on structure extended to entities: how organizations, people, and concepts are represented so both people and machines can identify and relate them correctly.
Turf Network — 2,500+ organizations across 142 entity-focused pages
- 05
AI-search research
As generative search began summarizing and citing information instead of only linking to it, the same entity and architecture questions became central to whether a brand is understood and recommended.
AI Visibility Diagnostic — a repeatable method for diagnosing AI-search visibility
- 06
AI-assisted workflows and systems
The current stage: human-in-the-loop workflows that make recurring analysis more consistent, without handing strategic judgment to automation.
AI-Assisted SEO Workflows — case study in progress
AI search extended my SEO practice rather than replacing it. Technical health, clear architecture, and well-defined entities still shape whether a system can interpret a brand correctly. What changed is how many systems are now doing the interpreting.
Method and perspective
Revision is part of the method, not a failure of it.
When an approach stops producing clarity, I change it. At Turf Network, long topical content clusters stopped serving the information model, so I removed them and shifted toward explicit entity pages and relationships. Content depth is not the same thing as information clarity.
I’m careful about what the evidence supports. In AI-search research I describe observations and working hypotheses, not ranking factors, and I state when a result does not establish cause.
Most of this work happens with clients and stakeholders. Research and technical findings only matter once they become roadmaps, presentations, and recommendations that marketing leaders, writers, designers, and developers can act on.
See the work
The evidence behind the story.
Now
Where the work happens today.
- SEO Strategist & Account LeadKSullivan Consulting LLC · 2018 to present
- I lead SEO strategy and account delivery across technical SEO, content strategy, Local SEO, information architecture, analytics, and AI-search visibility. My work spans research, strategy, implementation planning, reporting, and client communication.
- FounderTurf Network · 2023 to present
- An owned research project for testing entity architecture, structured information, and AI-search retrieval.
I’m interested in joining a team working on complex organic-search, AI-search, information-architecture, or search-product problems.
kevin@kevinsullivan.io