GEO Fundamentals
What is GEO?
Generative Engine Optimization, explained.
Your buyers now ask AI which vendor to choose. GEO is the work that decides whether the answer includes you, accurately, on every engine they use.
Fundamentals · Updated
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Generative Engine Optimization (GEO) is the practice of making your company legible to AI search engines like ChatGPT, Gemini, Perplexity, and Claude, so they cite you accurately when buyers research vendors. Resonate Labs runs GEO as a managed program for B2B SaaS: a monthly AI visibility audit, a 30-day action plan, and the execution support to ship against it.
The shift
Buyers now form their vendor shortlist inside an AI conversation, often before they ever reach a sales team.
The target
GEO optimizes for inclusion and accuracy in the AI's synthesized answer, across ChatGPT, Gemini, Perplexity, and Claude.
The work
On-domain structure, off-domain citation surfaces, and the schema that lets crawlers extract your claims cleanly.
GEO and traditional SEO are not the same work.
SEO optimizes for a ranking on a results page. GEO optimizes for inclusion and accuracy in a synthesized answer. That changes what matters. A page that wins on SEO can be invisible in GEO if it lacks the structural cues an AI model uses to extract claims: declarative opening sentences, self-contained paragraphs, explicit FAQs, schema markup that names entities and relationships.
Conversely, content that wins on GEO can underperform on traditional rankings if it prioritizes machine legibility over the engagement signals search engines historically rewarded. Most B2B teams need both, but the work is genuinely different, and the same agency or playbook rarely produces the best version of each.
Which AI engines matter for B2B buying research
The four that matter today are ChatGPT, Gemini, Perplexity, and Claude. Each has a distinct data diet: ChatGPT and Claude rely heavily on their training corpora plus selected real-time browsing, Gemini draws on Google's index, and Perplexity emphasizes its own retrieval layer on top of the open web.
Practically, this means optimization is multi-target. A B2B vendor that wants to be cited consistently needs both the underlying authoritative content and the off-domain signals that show up across all four retrieval paths. Optimizing for one engine in isolation tends to produce a brittle result.
How AI engines decide which vendors to cite
The deciding factors fall into three layers. First, retrievable presence: does authoritative, structured content about your category and company exist in places the AI engine can reach? Second, contextual fit: does that content cleanly match the specific question the buyer asked, in language the model can pattern-match against? Third, consensus signals: do multiple independent sources describe your positioning in similar terms?
AI engines treat repeated, consistent descriptions across review sites, analyst coverage, customer-facing content, and community discussions as a kind of soft validation. A category challenger with sparse coverage will lose to an incumbent with thorough coverage, even when the challenger's product is better.
Frequently asked questions
Can GEO work be measured?
Yes, but the metrics are different from traditional SEO. The primary measurements are visibility (how often your company appears in the AI's response for a defined query set), positioning (whether you appear as a top recommendation, a comparison, or a footnote), and accuracy (whether the AI describes your product and category correctly). Resonate Labs measures all three across a fixed set of 150 buyer-intent queries run against ChatGPT, Gemini, Perplexity, and Claude. Tracking month-over-month change against that baseline is what tells you whether the work is moving the right outcomes.
Where should a team start with GEO?
Start by understanding where you stand. Before writing new content or making structural changes, run a diagnostic against the queries your buyers actually use. Most teams discover one of three patterns: they are largely invisible, they are visible but mischaracterized, or they are visible and accurate but losing to competitors with better coverage. Each pattern calls for a different intervention. Skipping the diagnostic and writing more content first is the most common mistake, and it usually produces work that targets the wrong gaps. That is why every Resonate Labs engagement opens with a diagnostic: a free GEO Snapshot.
Do I need an existing GEO program to start?
No. Most engagements start from zero. The Foundation Review builds the knowledge graph and the query set from scratch, and the first 30 days surface where you stand, what's blocking you, and what to ship first. You don't need an existing GEO program. You need to start one.
Is GEO the same as "AI SEO," AEO, or LLMO?
You'll see the same shift called different things: AI SEO, Answer Engine Optimization (AEO), LLM Optimization (LLMO), generative search optimization. The labels differ; the underlying change is the same. Buyers are getting answers from AI engines instead of scrolling a results page, and vendors have to earn their place in those answers. Resonate Labs calls it Generative Engine Optimization (GEO) because the engines are generative: they assemble an answer rather than return a list.
Next step
See where AI puts you today.
A free GEO Snapshot maps your category and shows where you stand in AI answers today, and what to fix first.
- Which queries your buyers actually ask AI
- Where you're visible, cited, or absent today
- What the first 30 days would move