Generative Engine Optimization
Your brand has an AI problem. You just can't see it yet.
Somewhere right now, a buyer in your category is asking an AI for a shortlist. The answer comes back with three or four names. Yours isn't one of them. You'll never see this in your analytics. There's no click, no bounce, no referral string. The deal just never starts.
Generative engine optimization (GEO) is how you fix that. It's the practice of making your brand visible, credible, and accurately represented in AI-generated answers across ChatGPT, Perplexity, Google AI Overviews, and Claude. It's a parallel discipline to SEO, not a subset of it. 90% of ChatGPT citations come from pages ranking position 21 or lower in traditional search (Profound). The signals most marketing teams have spent years optimizing are nearly irrelevant to whether an AI model cites your brand.
Retina doesn't track what AI says about you. We change it.
How we think about the work
We start with the prompts real buyers use during shortlist formation, comparison shopping, and vendor evaluation. The queries that shape pipeline, not the keywords that look good in a dashboard.
ChatGPT and Perplexity retrieve and cite differently. Citation overlap between AI platforms and Google's top 10 is roughly 11% (Ahrefs). A strategy that works on one platform fails on the others. Each one gets analyzed independently, then synthesized.
We don't stop at "you were cited" or "you weren't." We figure out why. What content earned the citation, what gap cost you the mention, what your competitor published that you didn't.
Every finding maps to a specific action: revise, create, restructure, or build authority. Ranked by commercial impact. We build the assets, not just the briefs.
Every engagement starts with a structured knowledge graph of your specific market: competitors, buyer personas, features, pain points. That graph drives every query and recommendation. Nothing is templated.
What's under the hood
A six-phase analytical pipeline. Each step produces structured artifacts that chain forward.
Knowledge graph construction
We build a structured map of your market: competitors, buyer personas, features, pain points. This is the foundation everything else runs on.
Site analysis and technical audit
Parallel crawl for LLM accessibility and content inventory. If AI crawlers can't read your site, nothing else matters.
Client validation
You review the knowledge graph before we run a single query. We test the right questions, not our assumptions about your market.
Query generation
150 buyer-intent queries mapped across personas, buying jobs (8 stages), features, and pain points.
Cross-platform execution
Every query runs across the AI platforms where your buyers research vendors (currently ChatGPT, Perplexity, and Claude), baselined against neutral accounts to isolate what the platforms actually recommend, free from personalization noise. Hundreds of AI responses analyzed per engagement.
Analysis, recommendations, and execution
Visibility diagnostics, competitive benchmarking, citation analysis, and 120+ prioritized actions (typical). Then we build the content that closes the gaps.
What you get
Not a slide deck with observations. An operating plan.
Where you show up (and where you don't) by persona, buying stage, feature, and platform.
Who wins each query cluster, their share of voice, and what content is earning their citations.
Layer 1: technical fixes. Layer 2: existing page optimizations. Layer 3: new content to build. Ranked by effort and business impact.
Specific content blueprints: what to build, for which persona, on which platform, with on-domain and off-domain targets.
For ongoing clients, we build and deploy the content: page rewrites, comparison assets, spec content, competitive positioning pieces. Delivered monthly and measured against your baseline.
Why this approach is different
Most GEO platforms
Automate from citation patterns: what gets cited, template it, scale it
Recommend from generic signals: "add schema," "improve headings"
Track 10+ AI engines with broad coverage and shallow interpretation
Produce briefs users describe as needing heavy editing
Deliver the same value in Month 12 as Month 1
Retina
Diagnoses from buyer intelligence: which questions matter, why you're losing them, what to fix first
150 buyer-intent queries from your competitive landscape and buying stages
Prescriptive action plans ranked by commercial impact
Platform-specific: 89% citation divergence between ChatGPT and Perplexity
Compounds over time: by Month 6, we know which narratives win with your buyers
The methodology was built through enterprise audits across competitive B2B categories, formalized across four iterations of the GEO White Paper, and published in Cited, the first practitioner-grade book on GEO.
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