From SEO To GEO: How AI Search Really Sees Your Brand

Generative Engine Optimization (GEO) is quietly becoming the missing layer in most growth and dev marketing strategies.

We obsess over Google rankings and organic sessions, but barely track what happens when someone asks an LLM:

• “Best tools to audit my AI visibility”
• “Platforms to see how my brand appears in AI answers”
• “Alternatives to for AI search optimization”

If the answer never mentions your product, you have an AI visibility bug.

In this post I’ll outline how AI search actually sees your brand and a practical workflow you can use as a developer or technical marketer to run a GEO audit.

Why GEO is different from SEO

SEO optimizes documents for queries. GEO optimizes entities for answers.

Instead of asking “What keyword does this page rank for?”, GEO asks:

• How would an AI describe this product in 2–3 sentences?
• Which competitors does it list alongside it?
• What sources does it rely on for those claims?

LLMs don’t just index titles and H1s. They:

• Parse your schema, FAQs, docs and marketing site
• Pull signals from GitHub, app stores, docs, and blog posts
• Aggregate third-party content, reviews and comparisons

The result is an internal graph of entities and relationships. Your job is to make sure your brand node is clear, well-connected, and frequently cited.

What we see in AI visibility audits

Across audits we run, a few failure patterns are common:

1. Fuzzy positioning
   The site calls the product an “AI platform”, “analytics tool”, and “growth OS” in different places. LLMs end up with vague descriptions like “an AI-powered tool that helps with marketing tasks”. That doesn’t win you a slot in specific recommendation lists.

2. Weak machine-readable structure
   Pages look fine to humans but are thin on structure: few FAQs, inconsistent product names, no explicit “who this is for” or pricing bands. There is schema, but it doesn’t fully reflect how customers talk.

3. Thin external footprint
   The only strong sources are your own homepage and maybe LinkedIn. AIs prefer corroborated information. Without docs, GitHub, articles and comparisons, they either skip you or treat you as low-confidence.

4. Competitors owning the narrative
   Established SEO tools or GEO competitors show up by default. They’ve been written about, benchmarked, and cited for years. Even if your execution is better, models know far more about them than about you.

A simple GEO audit workflow for dev + marketing teams

You can get a surprisingly good picture of your AI visibility in a day.

Step 1: Collect real prompts

Sit with sales, support or founders and list actual questions people ask:

• “We need to see how our brand appears in AI search results”
• “We want to compare AI visibility across ChatGPT, Claude and Gemini”
• “Tools that automate structured optimizations for AI search”

Turn each into 1–2 natural-language prompts.

Step 2: Query multiple models

Run those prompts in the models your audience uses: ChatGPT, Claude, Gemini, Perplexity, Grok if relevant. Save the raw answers.

Step 3: Score what you see

For each model and prompt, note:

• Mention: are you in the answer at all?
• Position: if it’s a list, where do you appear?
• Description: does the summary match how you’d pitch the product?
• Neighbors: which competitors are listed next to you?
• Citations: which URLs or domains are being referenced?

A simple 0–2 scoring per dimension (0 = missing, 1 = weak, 2 = strong) is enough.

Step 4: Map gaps to concrete fixes

Some examples:

• Missing everywhere → tighten product naming, add FAQ schema, update meta descriptions and hero copy to be brutally clear about what you do and who you serve.
• Misclassified industry/use case → adjust copy, headings, and structured data to reflect the right category terms.
• No citations beyond your own site → create or update docs, GitHub repos, case studies and guest posts that restate your core value in plain language.

Step 5: Track over time

Re-run the same prompts monthly. Treat shifts (new mentions, better descriptions, different competitors) as a feedback loop for your GEO work.

Why developers should care

Much of GEO lives in implementation details that live with dev teams:

• Correct, consistent schema.org usage
• Clear canonical URLs and sitemap coverage
• Accessible, performance-friendly pages that AI crawlers can easily render
• Stable URL structures that won’t break accumulated understanding

If you already care about technical SEO, you’re 60–70% of the way there. GEO is about finishing the job: making sure AI systems can confidently understand and recommend what you’ve built.

Over the next posts I’ll go deeper into how to:

• Design FAQ and documentation that are “liftable” into AI answers
• Use structured data to reduce hallucinations about your product
• Measure model-by-model movement after you ship changes

If you’re working on AI visibility today, I’d love to hear what you’re seeing across different models.