How to Structure a GEO Team That Dominates AI-Driven Search

Search has changed. So should your team. If you’re still treating SEO like a checklist of tweaks and links, you’re going to fall behind – fast. AI platforms don’t care about keyword density or title tags the way they used to. They cite, summarize, and generate from content that’s structured for meaning, not just rankings.

A GEO team isn’t just a renamed SEO crew. It’s a strategic rebuild – pulling in roles like Relevance Engineers, Retrieval Analysts, and Prompt Engineers who understand how AI systems work under the hood. If you’re serious about staying visible in a world of chatbots, overviews, and zero-click queries, it’s time to rewire the team.

Rewriting Your SEO Team for an AI-First World

Back in the early 2000s, Netflix offered to sell itself to Blockbuster for $50 million. The execs passed. Who’d want to wait for DVDs in the mail when you could just visit your local store? That decision didn’t age well. Netflix is now worth over $200 billion. Blockbuster? It went bankrupt in 2010.

This isn’t just a throwback about disruption. It’s a warning – the same kind of thinking that sank Blockbuster is alive and well in marketing teams today.

Search isn’t what it used to be. AI-driven platforms are reshaping how people find and interact with content. And some of the most basic assumptions behind old-school SEO are falling apart.

• Rankings ≠ Revenue when users never visit your site
• Content ≠ Keywords × Volume when AI summarizes across sources
• More pages ≠ More traffic when answers come without a click

The ground is shifting. And most teams are still optimizing for a search engine that’s disappearing.

We’re no longer in the era where search engines just point you somewhere – they answer you directly. AI-generated responses are quickly becoming the front door to the internet.

The problem? Most marketing leaders still treat this shift like it’s some optional feature – not the structural rewrite it actually is. It’s a bit like those Blockbuster execs who couldn’t imagine anyone waiting for a DVD in the mail. That blind spot cost them everything.

Yes, Google still dominates traffic charts as of early 2025. But don’t let that comfort you. Under the surface, behavior is changing fast. More searches end without a click, and fewer users are visiting actual websites. The data doesn’t lie – organic clicks are steadily dropping, and AI summaries are taking their place.

AI-driven search isn’t coming – it’s already here. We’ve got AI Overviews, ChatGPT with browsing, Perplexity surfacing real-time citations, and Claude offering search as a conversation. These aren’t just features – they’re changing how content is discovered, interpreted, and credited.

This is where the split begins. Some companies are moving fast, rethinking their teams and adding new hybrid roles that blend SEO fundamentals with AI-native skills. Others? Still trying to patch old job titles into a new reality – and falling behind as a result.

That mismatch creates a widening gap. While some teams cling to outdated playbooks, the ones who adapt are already winning in platforms where traditional SEO doesn’t even show up.

Industry veteran Duane Forrester put it bluntly: “There’s a very defined layer of fear I feel. We have, say, hundreds of thousands of SEOs, but the vast majority – 97, 98% – aren’t ready for what’s happening now, or what’s coming next.”

Flip that around, though. If nearly everyone’s unprepared, the upside for the few who are is massive.

Now, just to be clear – classic SEO skills still matter. Strong content, clean UX, fast sites – those are still foundational. But that’s all they are now: a baseline. The real edge comes from mastering new layers like:

• Semantic content structure
• AI retrieval logic
• Entity and relationship mapping
• Multimodal formatting
• Cross-platform visibility tactics

This isn’t just SEO with new tools – it’s a full shift in how visibility works.

If you want your content to show up in AI search, it’s not enough to just be “optimized” – it needs to be engineered for relevance across every kind of platform, from Google’s AI Overviews to ChatGPT and beyond.

That’s where Generative Engine Optimization, or GEO, steps in. It builds on familiar ground – content quality, authority, structure – but takes things several layers deeper. This isn’t about tweaking headlines or sprinkling keywords. GEO brings a completely different approach to how content is built, structured, and deployed.

Shifting from SEO to GEO isn’t just a matter of learning a few new tools. It forces a rework of how your team is set up, what skills you prioritize, and how you define success in a space where clicks, rankings, and traffic don’t always tell the full story.

How NUOPTIMA Builds GEO Teams That Actually Work

At NUOPTIMA, we didn’t just tweak our SEO strategy – we rethought the whole playbook. As AI search tools like ChatGPT and Google’s AI Overviews began shaping how users find answers, we shifted early. Instead of chasing rankings, we focused on building systems that make our clients the answer.

That shift meant forming a GEO-focused team from the ground up. We’ve brought together prompt engineers, relevance specialists, data scientists, and AI-native content strategists who understand how large language models choose what to cite. Every piece of content we ship is built with retrieval in mind – structured, factual, and optimized for inclusion in AI-generated responses.

You’ll see the results not just in rankings, but in citations, mentions, and downstream conversions. Curious how this looks in practice? You can follow our team’s work and thoughts over on LinkedIn, where we share what’s working, what’s changing, and how we’re helping clients stay visible in a search landscape that’s rewriting itself in real time.

Future-Ready Roles: What the Shift Looks Like in Practice

So what does this actually mean on the ground? It means forward-thinking companies aren’t just tweaking job titles – they’re creating entirely new roles built for the AI search era. These aren’t repackaged SEO positions. They demand skills that barely existed five years ago, mixing technical expertise with a solid grasp of how generative systems work.

The hiring trends reflect this shift. Stanford’s 2025 AI Index Report shows that 78% of global companies are now using AI in production – up from 55% just two years ago. That kind of leap doesn’t happen quietly. It’s fueling a hiring boom, especially for roles tied to AI discovery and optimization.

So, where should companies actually start? Which roles matter most?

From what we’ve seen working with top brands in this space, a solid GEO team isn’t built around just one specialty. It’s a mix – each role tackles a different layer of how AI systems find, understand, and credit your content across tools like ChatGPT, Perplexity, Claude, and whatever comes next.

1. Relevance Engineer

This role sits at the heart of any GEO-first setup. A Relevance Engineer bridges the gap between old-school technical SEO and the kind of content structure AI systems actually understand.

Mike King coined the term back at SEO Week 2025. He explained it like this: most SEOs are still acting like mechanics – tuning engines, fixing parts, making tweaks. But that’s not enough anymore. We need engineers – people who design the entire machine from the ground up to work in an AI-dominated search world.

The issue, as Mike King put it, is that SEO became a culture of checklists – people just following old best practices without stopping to ask if they still make sense. But relevance isn’t something you bolt on. It has to be engineered from the ground up.

The Relevance Engineer doesn’t guess at what might work – they create structured systems that match how AI models interpret content. That includes everything from how pages are built to how data is labeled, so it all shows up cleanly across every platform, not just Google.

But here’s the thing: showing up isn’t the goal. Being chosen is. The job isn’t to be one of many – it’s to be the source AI models cite first, pull from, and trust. That’s the difference between being part of the conversation and shaping the answer.

Mike dives deeper into this in his “AI Mode” breakdown. His point? We’re no longer just formatting content to rank for a specific search – we’re designing it to slot into how AI thinks, reasons, and responds across a wide range of queries. It’s a different game entirely.

What a Relevance Engineer Actually Does

This role isn’t about ticking boxes – it’s about building systems that help AI models make sense of your content. Here’s what that looks like in practice:

• Semantic content architecture: Designing content so AI can grasp not just the words, but the meaning behind them. That means structuring around concepts and entities, not just keywords.
• NLP-informed content improvements: Using natural language processing tools to figure out how AI reads your content – and where it might miss the point. It’s not guesswork. It’s pattern recognition at scale.
• Retrieval-first structuring: Tuning both content and infrastructure so AI systems can actually find and pull the right information. Especially important when you’re working with RAG-based models.
• Running real tests, not just best practices: Formulating hypotheses and validating them – A/B testing fragments of content, trying different prompts, and tracking what gets picked up and what doesn’t. Real feedback, not gut instinct.
• Tracking performance beyond traffic: Measuring relevance with actual retrieval metrics – semantic similarity, citation frequency, AI visibility scores – not just rankings or bounce rates.
• Building the backend that makes this scale: Writing scripts, automating workflows, and creating custom tools that plug the gaps generic SEO platforms can’t touch. It’s what makes GEO function at the enterprise level.

A Relevance Engineer doesn’t think in pages – they think in systems. Their job isn’t to optimize one blog post at a time; it’s to build a structured network of content – text, video, audio – that AI can move through, understand, and connect the dots across.

So when an AI model scans your site, it doesn’t hit a wall of scattered posts or isolated ideas. Instead, it sees a well-mapped knowledge framework where each piece of content supports the next – organized in a way that makes it easy to process, retrieve, and cite with confidence.

What Relevance Engineers Need to Know

• A solid grasp of NLP: Understanding how machines interpret language, from semantics to entity recognition.
• Comfort with code: Python is usually the tool of choice – for automating analysis, building custom validators, or simulating how AI systems behave.
• Search that’s based on meaning, not just matching: Semantic optimization means shaping content that aligns with how AI measures intent, not just how people write queries.
• Strong content architecture instincts: Designing information systems that make sense to both humans and machines – clean structure, smart retrieval paths.
• Working knowledge of vector embeddings: Knowing how content is translated into mathematical space, and how to influence its position.
• Ability to measure what matters: From relevance scores to similarity metrics, they need to track performance in a way that AI systems actually “see” it.
• Prompt engineering as a testing tool: Using prompts to probe how different AI systems interpret, retrieve, or overlook your content.

2. Retrieval Analyst

If the Relevance Engineer builds the system, the Retrieval Analyst figures out how and why AI platforms choose what they surface. This role has quickly become essential – not just for getting content to rank, but for making sure it’s actually used by systems like ChatGPT, Perplexity, or Claude.

Here’s the hard truth: your content can look flawless, check every traditional SEO box, and still be completely ignored – simply because it’s not structured in a way AI can easily parse or cite. And if the model can’t pull it, it doesn’t matter how good it is. It won’t show up.

What a Retrieval Analyst Actually Does

This role is all about figuring out how AI models decide what to pull – and why some content gets cited while other pages get ignored. Here’s what that looks like in day-to-day practice:

• AI citation behavior: Digging into how different AI platforms pick sources. That means studying fan-out patterns – the background questions large language models generate – to see how they build context and decide what content makes the cut.
• Competitive visibility analysis: Looking at why a competitor’s content shows up in AI responses when yours doesn’t. The answer might be structural, semantic, or authority-based – the Retrieval Analyst’s job is to spot the gap.
• Platform-by-platform tuning: Each AI system plays by slightly different rules. This role tailors strategy for how content is cited and retrieved across tools like ChatGPT, Perplexity, and Claude.
• Tracking what really matters now: Instead of chasing traffic or old SEO KPIs, Retrieval Analysts measure things like chunk-level visibility and embedding relevance. As Duane Forrester puts it – we’re in a GenAI world now, and we need a new dashboard: think Chunk Retrieval Frequency, AI Citation Rate, Semantic Distance. That’s what actually moves the needle.

Retrieval Analysts don’t guess – they test. Their job is to run structured experiments that reveal how different AI platforms behave when selecting and surfacing content. Then they take those insights and turn them into clear, repeatable strategies that improve how – and where – your brand gets discovered.

It doesn’t matter whether someone’s using a traditional search box or talking to an AI assistant – the goal stays the same: make sure your content gets picked, cited, and seen.

What Makes a Great Retrieval Analyst

• Understanding AI platform behavior: How models retrieve, rank, and cite content across different ecosystems.
• Data analysis and trend spotting: Interpreting patterns across large sets of retrieval data – not just clicks and impressions.
• Competitor benchmarking: Figuring out why others are getting cited – and why you’re not.
• Testing and experimentation: Running controlled A/B experiments across platforms to isolate what’s working.
• Cross-platform measurement: Tracking how content performs on multiple AI surfaces, not just Google.
• Knowledge of query expansion and reasoning flows: Understanding how large language models generate internal queries and build context from them.

3. AI Strategist

The AI Strategist sets the direction for how a brand shows up in the age of generative search. While others are still focused on Google rankings, this role takes a wider view – mapping visibility across the full AI ecosystem: ChatGPT, Perplexity, Claude, and whatever else comes next.

They’re thinking about conversational discovery, mixed-media responses, and tailored outputs. Their job is to keep the brand consistent and discoverable across platforms – no matter the format, the prompt, or the user journey.

What the AI Strategist Actually Owns

This role isn’t about chasing short-term traffic spikes. It’s about shaping a long-term visibility plan that works across the evolving AI landscape. Here’s how that breaks down:

• Building a forward-looking roadmap: They plan ahead – mapping how shifts in user behavior and new discovery systems will impact how (and where) the brand gets found.
• Aligning across platforms: It’s not enough to optimize for one channel. The AI Strategist ensures the brand’s presence works across Google, ChatGPT, Claude, Perplexity – wherever discovery happens – all while keeping voice and messaging tight and consistent.
• Staying ahead of the curve: When a new AI feature or platform rolls out, this person is already on it. They help the team pivot before the competition even knows change is coming.
• Educating internally: They reframe how the org thinks about visibility. This isn’t just SEO 2.0 – it’s engineering for relevance in AI. And if leadership still sees GEO as a marketing side project, they change that mindset fast.

The AI Strategist is the one keeping an eye on the horizon. They figure out which platforms actually matter, what shifts are coming next, and how the team needs to adapt – not just technically, but strategically.

Their real skill? Turning complex AI mechanics into a roadmap the business can act on. They make sure GEO efforts stay tied to impact, not just activity.

Core Capabilities Every AI Strategist Needs

• Predicting how generative search is evolving: Reading trends in user behavior and search tech before they hit mainstream.
• Building strategies that span platforms: Making sure the brand shows up consistently – whether it’s Google, ChatGPT, or whatever comes next.
• Understanding how AI models work under the hood: Knowing what different models are capable of – and how those capabilities shape what content gets surfaced.
• Linking AI insights to business goals: It’s not just about visibility – it’s about using that visibility to drive real outcomes.
• Familiarity with agentic systems and multi-agent frameworks: As autonomous AI workflows become more common, knowing how these systems interact will matter more than ever.

Core Skills Behind Every High-Performing GEO Team

Switching from SEO to GEO isn’t just about tweaking tactics – it’s a shift in expertise. Your team needs to work with a different rulebook, because AI doesn’t index pages the way search engines used to. It decides what to surface, what to ignore, and what to cite – often without sending users anywhere at all.

Understanding NLP (Natural Language Processing)

If your team doesn’t understand how language works in an AI model, you’re flying blind. AI doesn’t rely on keywords – it relies on meaning. That means getting familiar with how models interpret semantic similarity, spot entities, and classify intent. These concepts shape how content gets retrieved, ranked, or left out entirely.

This shift isn’t new. Google’s been heading in this direction since the Hummingbird update and early vector models like Word2Vec. But most traditional SEO tools haven’t kept up – they’re still built around matching strings, not meaning.

For teams that do understand how AI maps relationships between topics and terms, this opens up a serious competitive advantage. It’s not about chasing keywords – it’s about structuring information in a way that fits how machines think.

Embeddings: How AI Actually Understands Content

AI doesn’t read like we do. It doesn’t skim a page or “understand” tone in the human sense. Instead, it breaks content into vectors – mathematical representations of meaning – and compares how closely those vectors align with a query.

This is what powers semantic search, RAG (retrieval-augmented generation), and the logic behind why one chunk of content gets surfaced over another. It’s not about keyword density anymore – it’s about proximity in vector space. That’s why content that looks average by SEO standards might get pulled first by an AI model.

To be competitive in GEO, teams need to wrap their heads around this. How do embeddings work? How does an AI model decide what’s “close enough”? And most importantly, how do you shape content so it sits in the right part of that space?

Mike King said it best – thinking in vectors isn’t optional anymore. It’s the foundation of how search now operates.

Why Python Matters in GEO

Not everyone on your team needs to write production-grade code – but having someone who can work with Python is a serious advantage.

Most SEO tools are still stuck at the page level, while AI models evaluate content in smaller, more targeted fragments. That’s where Python comes in. It gives your team the ability to build custom tools that operate at the same level AI systems do – analyzing individual passages, simulating AI retrieval patterns, and automating repetitive checks that would take forever manually.

With the right scripts, teams can run experiments, surface patterns no human could spot on their own, and test how content performs in real-world AI environments – not just in traditional rankings. It’s less about “being technical” and more about unlocking a deeper layer of visibility.

Structuring Content for How AI Actually Uses It

GEO strategy introduces a new layer to content planning: you’re not just writing for people anymore – you’re writing for AI systems that pull information from multiple sources, stitch it together, and serve it as a single, fluid response.

To show up in that process, your content has to be built for synthesis. That means structuring information in a way that models can easily digest – with clearly defined semantic chunks, clean subject-verb-object patterns, and enough context for each section to stand on its own if it gets lifted into a new response.

A helpful trick here? Create “fraggles” – short, self-contained passages designed to be extracted and cited by AI. Each one should answer a specific question while still fitting into the broader flow of the piece for human readers.

When tools like ChatGPT or Perplexity generate an answer, they’re pulling bits and pieces from different places – often sentence by sentence. If your content isn’t structured to be pulled apart, it’s far less likely to get included.

Prompt Engineering: Reverse-Engineering the AI Mind

As AI tools evolve, so does the need to understand how they think – and prompts are how you start the conversation. Writing effective prompts isn’t just about getting better responses; it’s how you test how AI systems interpret your content in the first place.

For GEO teams, prompt engineering is both a diagnostic tool and a strategic lever. It helps uncover which content types and structures get prioritized, and what gets ignored – giving you direct insight into the patterns AI models respond to.

But it goes deeper than that. Teams that invest in prompt testing start to see how different platforms – ChatGPT, Claude, Perplexity – interpret similar input in very different ways. And that’s critical for designing content that works across all of them.

It’s part experimentation, part architecture. You’re not just testing responses – you’re learning how to organize information in a way that machines consistently understand and cite.

Why Data Science Still Wins

If your team isn’t comfortable with stats, you’re going to struggle. Visibility in the GEO world isn’t about hunches – it’s about testing, measuring, and adjusting based on what the data actually says.

Understanding the basics – statistical analysis, A/B testing, data visualization – gives teams the ability to make confident decisions. Not guesses. When AI systems are working off billions of inputs, “best practices” don’t cut it anymore. You need to know what’s working, why it’s working, and how to improve it with evidence – not instinct.

This is where data science comes in. It helps teams spot trends no human could manually detect, validate whether strategies are paying off, and isolate weak spots across platforms. As rankings become less central and AI surfaces content in new ways, these skills are what keep performance measurable – and GEO efforts accountable.

Making Content Smarter with Knowledge Graphs

As AI gets more reliant on structured data, being able to build and manage knowledge graphs is becoming a serious advantage. It’s not just about organizing content – it’s about helping machines understand how everything connects.

Knowledge graphs map the relationships between entities, topics, and ideas. When done right, they make it easier for AI systems to process your content, draw context from it, and cite it accurately. You’re essentially giving the model a blueprint for how your information fits together.

To do this well, teams need to understand how entities relate to each other, how ontologies work, and how different AI tools represent internal knowledge. Once you’ve got that down, you can structure content in a way that matches how AI expects to see it – and that means better visibility, better citations, and more control over how your brand shows up in answers.

What a GEO-Ready Team Actually Looks Like

This is how smart companies are structuring their teams to stay ahead in the generative era – not just to rank, but to be retrieved, cited, and trusted by AI systems.

1. Head of GEO

The person setting the direction for all things GEO. They connect the dots between what the business wants and what AI platforms reward. This role typically reports to the CMO or Head of Growth and owns the strategy end-to-end – making sure the work actually ladders up to broader business goals.

2. Relevance Engineering Lead

Reporting into the Head of GEO, this role leads the team building the technical foundation – everything that makes content findable and usable by AI. They manage devs, analysts, and tooling experts to ensure all the moving parts – from schema to internal linking – are tightly aligned and working at scale.

Semantic Content Strategists

These are the people making sure your content makes sense to AI. They focus on structure, markup, and how entities are represented – not just to improve rankings, but to make sure machines can parse and process information properly. They work hands-on with writers and editors to layer in semantic logic without breaking the flow for human readers.

Structured Data Architects

Think of these folks as the translators between your website and AI systems. They handle schema markup and other structured formats, ensuring your content provides the right signals to machines. When done right, your pages speak a language AI understands – increasing the odds of being cited, not just crawled.

AI Discovery & Indexing Leads

Their job is to make sure your content doesn’t get lost in the shuffle. Traditional SEO focused on Googlebot. These specialists go broader – understanding how various AI platforms discover and index content, and optimizing accordingly. One crawler’s logic doesn’t always apply to another.

Infrastructure Engineers for Visibility

This team builds the backbone that supports both user experience and AI discoverability. They’re responsible for making sure your site runs fast, scales cleanly, and offers a structure that’s easy for both humans and machines to navigate. Every other role relies on the clarity and performance they engineer.

3. Head of Retrieval & Analytics

This role leads the team that turns data into decisions. While the Relevance Engineering Lead focuses on building the structure, the Retrieval & Analytics Lead focuses on performance – figuring out what’s getting retrieved, why it’s working, and where things need to shift. They’re the feedback loop that keeps GEO strategies grounded in reality, not just theory.

AI Visibility Analysts

These analysts track how your content actually shows up in AI systems. They’re keeping tabs on citation frequency, brand visibility, and how specific content chunks perform inside models like ChatGPT, Claude, and Perplexity. If your content is being ignored – they’ll know, and they’ll dig into why.

Applied Data Scientists

This team dives deep into model behavior using large datasets and predictive modeling. They build internal tools that help the rest of the team understand what’s happening under the hood – whether it’s analyzing semantic overlap, embedding distance, or how AI systems group related concepts. When others see noise, they find patterns.

4. Content Engineers

These are the builders behind large-scale content operations. Their role is to create systems that combine automation and human input – making sure even the most ambitious strategies can actually ship. They’re designing workflows, not just writing copy, and they think in terms of scale, structure, and long-term efficiency.

AI-First Content Creators

This crew writes with machines in mind. They break down information into semantic units that AI systems can easily interpret – but without losing the human touch. Their content is designed to be pulled, cited, and recombined, all while staying readable and relevant for actual people.

Prompt Engineers

Prompt Engineers are the ones who understand how different AI models respond to different types of input. They craft, refine, and test prompts across platforms to ensure brand consistency, accuracy, and retrievability. Their work helps the entire team better predict how content will be processed – and what’s likely to get surfaced in an AI response.

Ready to Build a GEO-First Team?

Hiring a few new roles or learning prompt syntax isn’t enough. Moving into GEO means rethinking how your entire team operates – not as marketers tweaking buttons, but as engineers building systems that work across multiple AI platforms.

Old-school SEO was reactive: tweak a title tag, stuff in a few keywords, earn a few links, then wait. That model’s broken. AI doesn’t reward that kind of guesswork – it responds to structured, intentional systems that understand how it thinks.

GEO is about designing for consistency. You’re not just optimizing content – you’re building processes that make it retrievable, citeable, and relevant at scale. That means testing constantly, adapting fast, and using data to steer every decision.

The teams that get this now – the ones that shift early, build with purpose, and speak AI’s language – will own visibility in the years ahead. Because here’s the truth: if your content isn’t getting retrieved, it doesn’t matter how good it is.

So ask yourself – do you want to be optimized for yesterday’s search… or engineered for tomorrow’s discovery?

FAQ

1. What does a GEO team actually do day-to-day?

It’s not about tweaking meta tags or chasing rankings anymore. A GEO team builds systems that help AI engines pick your content as the source of truth. That involves structured data, prompt testing, authority-building content, and a lot of experimentation. Think engineers, not just marketers.

2. How is a GEO team different from a traditional SEO team?

Traditional SEO teams optimize for how search engines rank websites. GEO teams design for how AI engines generate answers. The focus shifts from visibility in blue links to being embedded directly in the response itself. The tools, goals, and metrics are completely different.

3. Who needs a GEO team right now?

If your audience is using ChatGPT, Perplexity, or Google’s AI Overviews to find answers – and let’s be honest, they probably are – you need a GEO strategy. That doesn’t always mean hiring from scratch, but someone on your team needs to own this. Otherwise, you’ll fade from the conversation entirely.

4. How long does it take to see results from GEO work?

It’s not overnight. But it’s also not some vague, long-term investment with no feedback loop. With the right setup, you can start measuring AI visibility and citations in weeks. The real gains tend to show over a few months, as your content architecture gets aligned with what AI tools actually reward.

5. Can’t my SEO agency just do GEO too?

Some might try. But unless they’ve actively studied LLM behaviors, built for prompt injection, and run structured experiments inside AI engines – they’re guessing. GEO isn’t a light add-on to SEO. It’s a parallel discipline with its own workflows and its own rules.