GEO vs. SEO: Why Generative Engine Optimization Matters in the Age of AI
Generative AI search now pulls answers from just a few trusted brands. In this post, we share Amantru’s own experience and perspective on moving beyond ranking on SERPs to becoming the go-to name AI copilots cite first.
GEO is what happens when answers replace blue links
From AMANTRU’s vantage point, AI-native search experiences such as Google's AI Overviews, ChatGPT, Gemini, Claude, and Perplexity now synthesize answers instead of listing ten links. Semrush reports that Google's AI Overviews already appear on billions of searches each month and ChatGPT crossed 100 million users faster than any app in history, meaning a buying journey can begin and end inside an AI interface before a human ever reaches your site (Semrush, 2025). If AMANTRU isn't part of those synthesized answers, we're invisible at the exact moment of intent.
This post reflects AMANTRU’s current experience and take on how to adapt to that reality.
GEO vs. SEO: the goals diverge
| Dimension | Traditional SEO | Generative Engine Optimization |
|---|---|---|
| Primary goal | Rank a page high for a keyword | Earn a mention or citation inside AI-generated answers |
| Optimization target | Web crawlers and SERP algorithms | Multi-model assistants (ChatGPT, Gemini, Claude, Perplexity, AI Overviews) |
| Signal mix | Structured data, crawlability, backlinks, on-page relevance | All SEO fundamentals plus entity clarity, authoritativeness, freshness, quotes, and clean citations |
| Measurement | Rankings, organic sessions, click-through | Share of AI voice, percent of prompts citing us, sentiment inside AI summaries |
| Feedback loop | Search Console + analytics | AI visibility dashboards, prompt-level testing, citation monitoring |
In our experience, SEO work still matters—GEO is built on the same demand for helpful, well-linked content—but the success metric moves from “Do we rank?” to “Are we being quoted and recommended by AI guides?”
This is how we now frame the shift internally at AMANTRU.
AI models have their own editorial preferences
In our testing and in emerging GEO research, we see that generative engines surface content with specific patterns: pages that cite fresh statistics, contain direct quotes, are server-side rendered, appear in respected knowledge graphs like Wikipedia, and earn brand/name mentions across UGC platforms all receive outsized exposure (Semrush, 2025). For AMANTRU that means:
- Entity-rich storytelling. Anchor every launch and customer story with structured data, bite-sized stats, and quotable sound bites AI systems can reuse verbatim.
- Technical accessibility. Default to server-side rendering or static export for critical resources so crawlers that don't execute JavaScript still ingest our copy and metadata.
- Distributed authority. Seed credible mentions across analyst roundups, GitHub repos, Wikipedia, Reddit, YouTube explainers, and community forums so AI models see signals beyond our domain.
- Freshness as a feature. Ship changelog-style posts and data cuts monthly so LLMs detect recency and revisit our site more often.
These are the patterns we actively design for in AMANTRU’s own content.
Measurement looks more like radar than rank tracking
Alex Birkett's GEO tooling review notes that teams are adopting “visibility radar” platforms such as Peec AI, Goodie AI, Profound, and Semrush's AI Visibility Toolkit to benchmark how often different AI models surface their brand, which competitors are winning each prompt, and where citations originate (Birkett, 2025).
In our GEO experiments at AMANTRU, that kind of instrumentation closes the loop: we can target outreach at sources AI already cites, gauge sentiment, and spot blind spots before they impact pipeline.
A GEO playbook for AMANTRU
Here’s how we’re currently approaching GEO at AMANTRU:
- Audit our AI footprint. Use Semrush AI Visibility plus one multi-model tracker (Peec AI or Goodie AI) to benchmark how often AMANTRU appears for top 25 buyer prompts and which URLs get cited.
- Create cite-worthy assets. Launch a quarterly “Agent Readiness Index” complete with stats, downloadable tables, and executive quotes that AI agents can reuse. Pair each launch with a Wikipedia-friendly summary and a community AMA.
- Engineer entity clarity. Expand schema markup, author pages, and internal linking so every flagship solution has a canonical description, pricing point, and proof story that models can latch onto.
- Close citation gaps fast. When AI answers omit AMANTRU, inspect the sources they do cite, deliver expert commentary or co-marketing to those publishers, and monitor for subsequent inclusion.
- Report beyond traffic. Add “Share of AI voice,” “Number of prompts citing AMANTRU,” and “AI sentiment score” to our growth dashboard so leaders see GEO progress alongside classic SEO and pipeline KPIs.
This playbook will evolve, but it reflects how we’re operationalizing GEO today.
Bottom line
Traditional SEO still brings compounding value, but in our experience GEO is now the fastest path to discovery in an AI-mediated world. Brands that redesign their content, measurement, and outreach motions to feed generative engines are the ones AI copilots will remember—and recommend—first.
This post captures AMANTRU’s current perspective; we expect our approach to keep changing as AI-native search matures.
