Agentic SEO · led by Rob Garner
Agentic SEO, GEO & AEO strategy
Rankings used to be the finish line. Now your content gets retrieved, chunked, and cited by systems that answer instead of list. We build search strategy for that reality: making your site the source machines quote and humans trust. Then we keep watching how those systems treat you, because they change every month and a strategy nobody maintains is a strategy that expires.
The strategy layer is Rob Garner's — thirty years of search, applied to how retrieval systems read the web now. The thinking is public: we work through it, episode by episode, on The WorkHacker Podcast. And the parts that need software — pipelines, structured publishing, measurement — are built and operated by the same team, under content systems. One strategy, not a deck plus a handoff.
What this work looks like
AI-visibility audit
Where you appear — and don't — across Google, AI Overviews, ChatGPT, Perplexity, and the rest of the answer engines. We check what gets retrieved, what gets cited, and what gets paraphrased without attribution, then trace each miss to a cause: a page that doesn't chunk cleanly, an entity the knowledge graph can't resolve, a claim with no structure around it. The method is SERP-level linguistic analysis — not studying competitors, but reverse-engineering what the algorithm decided the answer was. The audit ends in a ranked fix list, not a hundred-page PDF.
Entity & schema modeling
Structured data and entity relationships that make your organization legible to knowledge graphs and LLMs. Schema is how you introduce yourself to a machine; most sites mumble. We model the entities that matter — the organization, the people, the products, the concepts you want to be known for — and connect them with consistent naming, sameAs links, and page-level markup, so a retrieval system can resolve who you are without guessing. Then we check that what the schema claims matches what the copy says, because machines cross-check now.
Content architecture
Taxonomy, internal linking, and page structure designed as semantic signals. Architecture is meaning: how pages connect tells retrieval systems what you're an authority on and what's an aside. We plan hubs, clusters, and anchor text deliberately — on larger sites with help from a private internal-linking engine that proposes links with natural anchors, routes every change through human approval, and measures the resulting lift in Search Console. It sits alongside the rest of our production systems.
Context-first content strategy
Context density over keyword density. A page should be so unambiguously about its topic that it passes the fill-in-the-blank test with the keyword stripped out. We build semantic coverage plans that capture stemmed and fanned-out searches — the query variants an answer engine generates on the user's behalf — and write briefs your team can execute, or that our content pipelines can execute for you. Precision beats volume: verbose pages dilute the exact context retrieval systems select for.
Measurement & reporting
AI visibility doesn't show up in any one dashboard, so we triangulate: Search Console impressions and position, AI Overview presence, citation checks against a fixed question set run on the major assistants, and referral patterns from answer engines. Each month you get what changed, what likely caused it, and what we're adjusting in response — the same reporting discipline we apply to the automation we run for clients.
How an engagement runs
Audit and baseline
We run the AI-visibility audit and take baselines before touching anything: rankings, AI Overview presence, citation checks against a fixed question set, Search Console exports. Nothing gets recommended without a before-number attached, because in month four we want to argue from evidence, not memory.
Build and calibrate
Schema and entity modeling ship first — everything downstream depends on machines being able to resolve who you are. Architecture changes and content briefs follow, each routed through your team's approval. We don't push edits to a production site without a sign-off, ours or yours.
Run and report
Strategy becomes a running system: briefs executed, internal links proposed and approved, schema kept in sync as pages ship. Monthly reporting covers movement against baseline and what we changed in response. Retrieval systems shift monthly; the strategy adjusts with them instead of expiring quietly.
Recalibrate
Every quarter we re-run the audit against the original baseline, retire tactics that stopped mattering, and re-rank the fix list. What generative engines rewarded two years ago is not what they reward now; a fixed playbook is how visibility erodes without anyone noticing.
Proof, with numbers
36
episodes · six platforms
The WorkHacker Podcast
The strategy on this page, argued in public — episodes on retrieval mechanics, context density versus keyword density, schema and entity modeling, and architecture as meaning. Produced end to end by our own automated pipeline.
313
consecutive days · 160+ entities
Descout
An automated newsroom whose coverage is structured for machine retrieval from the start — entity-mapped, consistently formatted, published daily under editorial rules enforced in the pipeline.
Runs on our own properties first · human-approved, reversible
A private internal-linking engine
Reads a site the way retrieval systems do, proposes internal links with natural anchor text, routes every change through human review, and measures its own lift in Search Console — before and after, per link.
Why us
- Rob has been in search since the mid-1990s — seven years as VP of Strategy at iCrossing (Hearst), then Director of Content Services, North America at iProspect. The full history is on the about page.
- He wrote the book on integrating search and content: Search and Social (Wiley, 2012), plus 150+ columns for MediaPost's Search Insider.
- Agency-side strategy for Marriott, Ritz-Carlton, Mastercard, Visa, USAA, and Ally Bank.
- Still publishing at the front edge — current Search Engine Land bylines on GEO, AIO, llms.txt, and context-first AI search strategy, and a weekly working session on the podcast.
SEMPO board VP 2010–2014 · DFWSEM co-founder · Speaking at SES, SMX, Pubcon, SXSW since 2004
Common questions
07 Questions
What is agentic SEO?
Agentic SEO is search strategy for a web where AI agents retrieve, evaluate, and cite content rather than just ranking it. The practical work is making a site legible to those systems — clean chunking, explicit entities, structured data, dense context — and then measuring whether they retrieve and cite you. It includes traditional SEO rather than replacing it; Google is still the largest retrieval system there is.
How is GEO different from SEO?
GEO — generative engine optimization — optimizes for being cited in generated answers, where classic SEO optimizes for ranking in a list of links. The mechanics overlap, but the unit changes: retrieval systems select passages and entities, not pages and keywords. A page can rank well and still never be quoted, because it contains no passage worth quoting. GEO fixes that.
What is answer engine optimization (AEO)?
AEO is structuring content so answer engines — AI Overviews, ChatGPT, Perplexity, Copilot — can lift an accurate, attributable answer from your pages. In practice: direct first-sentence answers, question-shaped headings, schema that supports the claim, and entity consistency so the system knows who is answering. It sits next to GEO; we treat them as one discipline with two measurement surfaces.
Do AI answer engines read schema?
Engines that retrieve live pages — Google's AI Overviews, Perplexity, Copilot — use structured data through the same indexes that feed search. Schema won't rescue thin content, but it disambiguates entities, connects your pages to knowledge graphs, and makes claims machine-checkable — all of which lowers the cost of citing you. We treat it as required plumbing, not a growth hack.
How do you measure AI visibility?
We triangulate, because no single dashboard reports it. A fixed question set run against the major assistants tracks citations over time; Search Console tracks impressions and position, including AI Overview surfaces; referral data catches assistant traffic. Every engagement starts with baselines so change is attributable. And we stay honest about limits — sampling assistants is sampling, not a census.
What is context density?
Context density measures how unambiguously a page is about its topic — the working replacement for keyword density in retrieval-based search. The test: strip the main keyword out and see whether a competent reader, human or machine, can still fill in the blank. High-context pages get retrieved for stemmed and fanned-out queries they never explicitly targeted.
Do we still need traditional SEO?
Yes — the same crawling, indexing, and authority signals that fed rankings now feed retrieval. A site that is slow, poorly structured, or thin fails in both systems for the same reasons. What changes is the target: we optimize for being the retrieved, cited source, and treat rankings as one surface of that rather than the goal itself.
Working together