Traditional SEO is necessary but no longer sufficient. AI search engines — ChatGPT, Perplexity, Claude, Google AI Overview — are quietly becoming the first stop for high-intent B2B buyers. We optimize for both, because the businesses that win 2026 search show up in both.
Traditional SEO still drives the majority of organic search traffic. It always will, for queries with clear single-page intent. But for research queries — "best [category] for [use case]," "alternatives to [tool]," "how do I [solve this problem]" — buyers increasingly ask AI assistants first.
Princeton's GEO research found that comparison content drives roughly 33% of all AI search citations. The businesses cited in those comparisons end up in the AI's recommendation set. The ones that aren't, don't — and the buyer never even visits Google.
The good news: most of the work is shared infrastructure. Clean schema, fast pages, accessible content, structured comparisons, machine-readable context files (llms.txt). Do the work once, win in both surfaces.
Crawl audit, indexation health, schema deployment (Article, FAQPage, Product, BreadcrumbList, Person, Organization), Core Web Vitals work, sitemap + robots, canonicals, structured redirects.
llms.txt deployment, comparison content cluster, structured Q&A markup, citation-friendly content patterns, allowlist for AI bot crawling (GPTBot, ClaudeBot, PerplexityBot, Google-Extended).
Founder-written, honest-voice comparison pages between you and your competitors. The content type AI engines cite most. Each one is also a high-converting sales page.
Digital PR placements in articles AI assistants are already citing. Outreach to roundup-article authors. Featured snippet captures. Strategic backlink building.
Monthly tests of brand queries across ChatGPT, Perplexity, Claude, and Google AI Overview. GA4 segment for AI referral traffic. Traditional rankings tracked alongside.
Keith runs every program. No outsourced content writers. No SEO templates. No gibberish content built to game algorithms — it has to read well to humans first.
Technical SEO audit. AI citation baseline (where do you currently appear in ChatGPT / Perplexity / Claude / Google AI Overview answers?). Schema deployment. llms.txt. Core technical fixes.
Comparison content cluster. Service pages. Pillar content. Internal linking architecture. The content layer AI engines will cite and Google will rank.
Outreach to roundup authors and high-authority publications. Featured snippet captures. Backlink building. The work that compounds rather than the work that decays.
Ongoing optimization. New content based on emerging keyword and citation patterns. Schema expansion. Steady authority building. The phase where AI citations and traditional rankings start moving together.
Traditional SEO optimizes for ranking in Google's blue links — keywords, backlinks, technical health, on-page structure. AI search optimization (also called AEO, GEO, or LLMO) optimizes for being cited by AI assistants like ChatGPT, Perplexity, Claude, and Google's AI Overview. They share infrastructure (clean schema, fast pages, accessible content) but the surface area differs: SEO targets a 10-blue-link page; AI search targets a generative answer that names specific sources. Most buyers in 2026 use both — and the businesses that show up in both win.
Two reasons. First, Google's own AI Overviews now appear above the blue links for an increasing percentage of queries — getting cited there is the new featured snippet. Second, the buyers ahead of the curve (especially in B2B SaaS, agencies, and high-intent service categories) are already running their first research pass through ChatGPT or Perplexity. By the time they get to Google, they have a shortlist. If you're not on that shortlist, you don't get the click.
Three measurement layers. (1) Citation tracking: monthly tests of brand-relevant queries across ChatGPT, Perplexity, Claude, and Google AI Overview to see whether and where you're cited. (2) AI referral traffic: GA4 segments tracking sessions from chatgpt.com, perplexity.ai, and similar referrers. (3) Brand mention frequency: external listening (forums, comparison articles, Reddit) for how often you're named as a category option. Traditional SEO metrics (rankings, organic traffic, backlinks) are still tracked alongside these.
Audit phase: technical SEO health check, schema audit, content gap analysis, AI citation baseline. Build phase: schema markup deployment (Article, FAQPage, BreadcrumbList, Person, Organization, Product), comparison content cluster, llms.txt for AI agents, technical fixes (sitemap, robots, canonicals, redirects), Core Web Vitals work. Authority phase: digital PR placements in articles AI assistants are already citing, structured data expansions, on-page optimization for the queries that matter. Reporting: monthly citation tracking + traditional rankings.
Traditional SEO timelines: 3-6 months for measurable ranking movement, 9-12 months for compounding traffic. AI search timelines compress this somewhat because the corpora rebuild faster than Google's index ranks — a well-cited mention in a roundup article can show up in ChatGPT/Perplexity citations within 4-8 weeks. The combined-program ROI typically becomes obvious in months 4-6 and accelerates from there.
Scoped per engagement based on site size, current state, and competitive intensity. Typical monthly retainer for a managed program (audit, builds, ongoing optimization, monthly reporting) starts in the low four figures and scales with ambition. Standalone audits and one-off builds (comparison cluster, schema deployment, llms.txt) are also available as project work. Book a 15-minute call to scope.
We'll run brand queries against ChatGPT, Perplexity, Claude, and Google AI Overview to see whether (and where) you're currently cited — plus a one-page traditional SEO audit. No obligation. Just the baseline.