Dual optimization: tackling SEO and GEO together

A split is happening within AI that most visibility strategies ignore. On one side you have AI that searches the web live and cites sources. On the other side you have AI that only answers from what it learned during training, without ever consulting the web. Dual optimization is my term for the approach that serves both worlds at once: one strategy for what AI finds on the web, and one for what AI already knows from its training data.

In practice, that means tackling your SEO and your GEO (generative engine optimization, making your brand visible in AI answers) together instead of as separate projects. In this article I explain why that is necessary, how the two worlds differ, and where they reinforce one another.

Measure it yourself: see how ready your page is to be cited by AI with the free GEO check.

Two worlds where your prospect looks for you

Your potential customer moves through both worlds, often without realizing it.

A prospect asks ChatGPT with its search function about solutions in your category and sees your content cited. That same prospect works at a company with an internal AI assistant without web access, which answers the same question from pure training knowledge. In that second situation you can be entirely absent, even if you score perfectly in the first.

That is exactly the risk of serving only one world. Anyone who bets solely on the web world is invisible in internal AI deployments, in offline and embedded AI, and vulnerable whenever retrieval fails. Anyone who bets solely on the training world builds up slowly and misses all the real-time opportunities.

The public AI world (with grounding)

These are systems that fetch current information from the web:

• Platforms: Google AI Mode and AI Overviews, ChatGPT with browsing, Perplexity, Claude with its search function, Microsoft Copilot with web access.
• Characteristics: real-time retrieval from indexed content, citations that link to your page, and freshness that genuinely counts.
• Visibility mechanism: your content is retrieved, evaluated and possibly selected. Your selection chances, extraction quality and citation coverage determine whether you make it into the answer.

This is largely the territory of SEO, supplemented with AI-specific techniques. The levers are content quality, clear structure, freshness and authority signals.

The private AI world (without grounding)

These are systems that only answer from their parametric knowledge, meaning the knowledge held in the model weights:

• Deployments: internal company assistants, embedded AI in products without a search function, offline applications, and API usage without tools.
• Characteristics: no real-time retrieval, answers limited to the training data, no citations to current sources.
• Visibility mechanism: your brand appears only if it sits in the model weights. Brand salience and the strength of your associations determine whether you get mentioned.

Here, classic content optimization does not help directly. The levers are presence in sources that models are trained on, a consistent message across all those sources, and authority in the right publications.

Why both worlds matter

You might think: isn’t public AI with a search function simply the future? Isn’t everything drifting toward grounding on its own? Not necessarily, and even if it were, that transition will take years.

Private AI is ubiquitous today. Large organizations roll out AI internally with little or no web access, often for security and compliance reasons or simply because of cost. When an employee asks such a system about suppliers or solutions, it answers from training knowledge. If you are not in there, you do not exist for that employee.

On top of that, training data also plays a role when there is grounding:

• Selection preference: models more often choose content from brands they already “know” from training, even when active retrieval takes place.
• Confidence in the phrasing: about brands with a strong training presence, a model speaks more confidently. A weak presence produces cautious, hedged language, even if your content is selected.
• Context and framing: training data determines how a model interprets and frames the retrieved information.

In other words: your private presence colors how well your public optimization works. That is why I see dual optimization not as a luxury, but as strategic completeness. If you want to dig deeper into that foundation, also read the ultimate GEO guide.

SEO vs GEO vs together: the comparison

The table below sets the three side by side so the difference is immediately clear.

The public strategy: winning at retrieval

The core goal here is to maximize your selection chances and citation quality when AI systems retrieve and evaluate web content. I break it down into four layers:

1. Retrieval optimization. Make sure your content gets retrieved at all: broad topic coverage, a strong SEO foundation (indexing, authority), freshness and a question-oriented structure.
2. Selection optimization. Win the selection once you are retrieved: semantic compression so you are highly extractable, high information density, direct answers and clear headings that mirror the question.
3. Extraction optimization. Maximize extraction quality once you are selected: self-contained fragments that hold up on their own, explicit naming of entities and strategic placement of key information.
4. Platform optimization. Adapt to platform-specific behavior and monitor your performance across the different AI search products.

The advantage of this world: results are relatively quick to see. Content changes count within days to weeks, and you can keep adjusting continuously. You will find more on the distinction between these two disciplines in GEO vs SEO.

The private strategy: being present in the training data

The core goal here is to build strong brand associations in the training data, so models mention and recommend you even without retrieving anything. Here too I break it down into four layers:

1. Presence in training sources. Appear in sources that likely weigh in during training: encyclopedias like Wikipedia, established news media, academic publications, institutional sources and leading trade media.
2. Consistent association. Ensure a single message across all those sources: the same positioning, the same category association, the same link between your brand and your core attributes. Contradictory messages create confused associations.
3. Authority building. Build signals that translate into training: recognition by experts, speaking and publishing spots in authoritative channels, and relationships with analysts.
4. Sustained presence. Stay visible across multiple training cycles. Not a one-off campaign, but a continuous presence in relevant sources.

This is a long-term game. The influence on training data takes months to years, and model updates follow irregular schedules. But the results are durable. What GEO precisely involves, you can read in what is GEO.

Where the two reinforce each other

Dual optimization is not two separate efforts, but an integrated approach where each strategy feeds the other. I see two reinforcing loops:

• From public to private. Your content gets cited in AI answers, people share and discuss those answers, those discussions land in the training corpus, and future models thus learn your associations. Your private visibility grows along with it.
• From private to public. A model with strong brand associations recognizes you during retrieval, that recognition creates a favorable evaluation bias, your selection chances rise, and more citations reinforce the cycle again.

That first loop means your public visibility of today builds your private visibility of tomorrow, in four steps that keep reinforcing themselves:

Some content, moreover, serves both worlds at once. A thorough guide on your own site gets retrieved and cited (public), can be cited by other publications (and thus land in training sources), and establishes expertise that counts in both worlds. Original research does exactly the same: it delivers unique value for selection and gets picked up by authoritative media.

How to divide your resources

For most organizations, a phased approach works best. Start with an emphasis on the public side for quick wins, while building the private infrastructure in parallel. Shift gradually toward balance as your authority matures.

• More emphasis on public if you still have little visibility, possess strong content capacity and need to react quickly to competition.
• More emphasis on private if you already score strongly in public, have access to authoritative publishing channels and maintain a long-term view.

A good content structure helps in both worlds, because fragments that hold up on their own are extracted more smoothly. You can read more about that in site architecture for SEO.

The enterprise blind spot

Finally, I want to highlight one surface that most strategies miss: internal AI in large organizations. When an employee asks their internal AI “what are the best solutions for this category?”, the answer comes from the model’s base training, any internal fine-tuning and internal documents. Your web content does not count there. Your latest press release does not matter. Only your presence in the training data, and possibly in the company’s internal knowledge base, determines whether you show up.

For categories where enterprise buyers matter, that means: invest in influencing the base training (all the private strategies above), get your content into sources that companies already trust, and ensure a presence in partner and analyst content that weighs in on their decisions. That is precisely the visibility you would never reach with a purely SEO-focused approach.

Frequently asked questions

What is dual optimization exactly?

Dual optimization is optimizing at the same time for two kinds of AI: systems that search the web live and systems that only answer from their training data. In practice you combine your SEO and your GEO (generative engine optimization) into one integrated approach, so you stay visible no matter how someone uses AI.

Do I have to choose between SEO and GEO?

No. Anyone who serves only one world leaves gaps: SEO alone makes you invisible in internal and embedded AI, GEO alone misses all the real-time opportunities. The two also reinforce each other, so the gain lies precisely in the combination. A strong SEO foundation is directly the base on which you build your GEO.

Why does private AI without web access still matter?

Because a large share of AI interactions runs parametrically. Companies roll out internal assistants without web access, AI is embedded in products without a search function, and even on public platforms grounding is not always active. In all those cases you appear only if you sit in the training data.

Where do I start if I want to tackle both?

Start with the public layer, because that is where you get the fastest result: make sure your content is retrieved, selected and well extracted. Build your authority and a consistent message in authoritative sources in parallel, because that parametric visibility takes time but delivers durable results.

Need help?

Want to translate this into execution? See how we approach it with AI findability.

Further reading

• How the new AI search architecture works (retrieval, generation and RAG)