Here’s a draft blog post written in a first‑person “agency voice” you can publish and then adapt to your tone.
GEO vs SEO: How We Actually Optimize for Generative Engines in 2026
If you work in search, you’ve probably noticed something: your traffic reports still say “organic search,” but your prospects are getting answers from overviews, AI snapshots, and chatbots long before they hit your site. Generative Engine Optimization (GEO) is how we close that gap at our agency, and it’s already reshaping the way we do SEO for clients.
In this post, we’re going to break down how we think about GEO, how it relates to traditional SEO, and the practical steps we take to get our clients surfaced, cited, and trusted inside generative experiences.
GEO is not a rebrand of SEO
We do not treat GEO as a new religion or a clever rebrand of what we’ve always done. We treat it as a new layer on top of existing SEO fundamentals.
-
SEO is about earning visibility and clicks from ranked search results. GEO is about earning inclusion, citations, and narrative control inside AI‑generated answers.
-
SEO measures impressions, clicks, rankings, and conversions from search. GEO adds “answer presence”: how often engines mention, quote, or lean on your brand when they generate responses.
-
SEO is still about crawlability, content, links, and entities. GEO adds context, coverage, and consensus: are you part of the “shortlist of sources” LLMs keep coming back to?
In our client work, we don’t replace SEO with GEO – we use GEO to decide where to over‑invest in content, entities, and distribution so that both search and generative engines see the brand as a canonical source.
How we define GEO in our agency
Internally, we use a simple definition:
GEO is the practice of making your brand the most likely source of record when generative engines answer questions in your domain.
That definition forces us to answer three questions for every client:
-
Which questions, topics, and tasks do we want AI systems to associate with this brand?
-
Which assets (pages, PDFs, videos, communities, tools) should be optimized so they are fetched, summarized, and cited by those engines?
-
Which signals (links, mentions, entities, structured data, user behavior) do we need to amplify so those assets become “safe defaults” for the models?
When we say “we’re doing GEO,” we’re not talking about adding a new meta tag. We’re talking about designing an ecosystem around those three questions and doing the boring work to support it.
The GEO stack we use with clients
Here’s how we implement GEO in real campaigns.
1. Map prompt space, not just keyword space
We still do keyword research, but we also map prompt space:
-
What complete questions are people asking tools like ChatGPT, Perplexity, and Gemini in this niche?
-
How do those questions differ from classic search queries?
-
Which of those questions, if we “owned” the answer, would move revenue for the client?
We collect prompts from support tickets, sales calls, community threads, search queries, internal site search, and public Q&A platforms. Then we cluster them by intent and complexity, not just by keyword.
2. Audit the current “answer graph”
For each high‑value prompt cluster, we look at:
-
Which domains are being cited and linked inside AI answers
-
Whether Reddit, docs, GitHub, YouTube, or niche communities show up again and again
-
How often brand‑level entities (people, products, companies) are named, even when not linked
This tells us who currently owns the “answer graph” and what kind of content wins: reference docs, playbooks, opinionated explainers, tutorials, comparisons, or tooling.
3. Build canonical answer assets
Once we know the prompt landscape and the current answer graph, we design canonical assets to target:
-
Deep, evergreen explainers that match how humans phrase questions and how models summarize topics
-
Highly structured guides (sections, steps, FAQs, tables, code samples) that are easy for LLMs to chunk and reuse
-
Supporting content (checklists, templates, calculators, frameworks) that gives engines something concrete to cite
We still optimize these for classic SEO: intent alignment, internal linking, schema markup where it matters, and solid on‑page fundamentals. But we write and structure them with an additional constraint: “Would an LLM find this easy and safe to reuse?”
4. Optimize entities and structure
GEO without entity work is just content marketing with extra steps. For our clients, we:
-
Clarify and reinforce key entities: brand, founders, products, features, industries, and use cases
-
Use schema markup where it creates clarity (Organization, Product, FAQ, HowTo, Article)
-
Ensure that author profiles, about pages, and documentation make it unambiguous who does what, for whom, and where
This isn’t about pleasing an E‑E‑A‑T checklist. It’s about making it trivial for models to understand “who you are” and “when to talk about you.”
5. Earn signals in the places LLMs trust
We still care about links from high‑quality sites, but GEO forces a broader view of what a “signal” is:
-
Citations and mentions in niche communities that get scraped and reused
-
Inclusion in curated resources, comparison posts, and “best tools” lists
-
Public documentation, GitHub repos, and open knowledge bases that models frequently train on or crawl
Our GEO campaigns often involve Reddit, industry Slack/Discord communities, technical docs, and long‑form educational content that’s written to be scraped – not just read.
How we measure GEO impact
Right now, measuring GEO is messy but not impossible. We look at:
-
Answer presence: How often does the brand or domain appear in AI answers for our target prompts?
-
Citation quality: Are we cited as a primary reference or just a footnote among many?
-
SERP overview inclusion: In engines that show AI overviews, how often is our content pulled in or quoted?
-
Brand lift in search: Are we seeing more branded search around the concepts we’ve been seeding into generative answers?
-
Commercial outcomes: Are sales calls and demo requests referencing “I saw you mentioned in X” or “an AI tool recommended you”?
We don’t pretend these numbers are as clean as rank tracking, but they’re directional enough to tell us whether we’re becoming part of the default “shortlist” in the model’s mind.
What this means for your SEO strategy
If you’re an in‑house marketer or founder, you don’t need to rip out your SEO program and start over. But you probably do need to change the questions you ask:
-
Instead of “What keywords should we rank for?” ask “What questions should we own in both search and chat?”
-
Instead of “How do we get more backlinks?” ask “Where do models already look when they answer questions in our niche, and how do we show up there?”
-
Instead of “What’s our blog calendar?” ask “What are our canonical answer assets, and how do we support them across formats and channels?”
GEO is not the end of SEO. It’s the reality check that reminds us why we were doing SEO in the first place: to be the most trusted answer when someone has a problem we can solve.
If you’d like us to map your GEO landscape and show you where generative engines already talk about your brand – and where they should be talking about you but aren’t – we can run a focused GEO + SEO diagnostic and turn it into a concrete action plan.
What audience are you primarily targeting with this post right now: SaaS founders, in‑house marketing leaders, or other agencies?
