Foundations

Frameworks & Philosophies: Building a GEO Strategy for the AI Era

Sathvik Tantry16 min read

As we’ve explored the mechanics, case studies, and data behind Generative Engine Optimization, it’s clear that optimizing for answer engines is a multidisciplinary challenge. It’s part SEO, part content strategy, part PR, part data science.

In this article, we’ll step back and outline a framework for GEO – essentially, a blueprint equivalent to SEO best practices, adapted for the AI-driven search landscape. We’ll also discuss guiding philosophies to approach this new area in a sustainable, ethical, and effective way.

Why We Need a GEO Framework

In the early days of SEO, practitioners created frameworks (like the well-known division of Technical SEO, On-Page SEO, and Off-Page SEO) to organize their efforts. It helped ensure all bases were covered. Now, with GEO, there’s a similar need. We’re dealing with:

  • New technical considerations (AI crawlability, structured data for AI, etc.)
  • Evolving content strategy (writing for an AI audience as well as humans)
  • Influence and authority beyond traditional SERPs (ensuring your brand is part of the broader knowledge ecosystem)

Rather than tackling these randomly, a framework provides a checklist and a philosophy to prioritize what matters.

The GEO Framework: 5 Pillars of Success

Based on current knowledge, we can propose five key pillars for GEO strategy – analogous to SEO’s traditional pillars – with some overlap and some new elements:

  • Research & Analysis
  • Technical Foundations
  • Content & Context Strategy
  • Brand Presence & Authority
  • Monitoring & Adaptation

Let’s break each down.

1. Research & Analysis (Understand the Landscape)

Every good strategy starts with understanding the playing field. For GEO, this means:

  • Query Landscape Analysis: Identify the questions and conversational queries in your niche. Use methods we discussed (Google’s People Also Ask, forum FAQs, keyword tools with question filters) to gather what potential customers might ask an AI. This is similar to keyword research but focused on natural language and long-tail, multi-part questions.
  • AI Behavior Research: Spend time querying different AI platforms (ChatGPT, Bing Chat, Google’s Bard) with your industry questions. Observe how they frame answers, what sources they cite, and where they might be hallucinating or omitting information. For example, you might notice Bard gives very brief answers with direct sources, whereas ChatGPT gives longer, synthesized answers without citations. These nuances inform how you optimize (e.g., maybe Bard is more likely to use exact phrasing from a source, so exact match questions on your site could help).
  • Competitor GEO Audit: Just as you’d audit competitors’ SEO (their rankings, content, backlinks), audit how competitors appear in AI answers. Ask the AIs: “Who are the top providers of X?” or “What’s the difference between [Your Brand] and [Competitor]?” and note if your competitors are mentioned where you are not, or vice versa. If a competitor keeps showing up in answers, research why – do they have a stronger content presence, or more mentions on external sources? This analysis highlights opportunities (and threats) for your GEO plan.
  • Data Analysis: Use any available data from tools (as discussed in the Tools article) to quantify your starting point. Maybe SpyGPT data shows your brand appears in only 2% of relevant questions, whereas a competitor appears in 10%. Establish a baseline so you can measure improvement.

In short, know the questions, know the players, know the platforms. This pillar mirrors traditional SEO research, and its importance cannot be overstated. You can’t optimize what you don’t understand.

2. Technical Foundations (Be Discoverable to AI)

Technical SEO doesn’t go away in the AI era; it evolves. Under this pillar:

  • Crawlability for AI: Ensure your site is accessible to AI crawlers. This means checking robots.txt (don’t accidentally disallow GPTBot or others unless you intend to opt out) and considering a llms.txt file or similar. Technical openness is the prerequisite for inclusion – if the AI can’t easily retrieve or ingest your content, it won’t use it.
  • Structured Data & APIs: Structure your data so AIs can easily digest it. Use schema markup (FAQ, HowTo, Product, etc.) – AI thrives on structured data. Also, if relevant, provide data via APIs or feeds. For instance, if you have an extensive database (like job listings, product specs, etc.), consider offering an API or participating in datasets that AI might use. (E.g., Yelp’s API for restaurant info – if you ensure your data is correct in those feeds, an AI pulling from them will relay accurate info.)
  • Site Speed and Indexation: These classic factors still matter. Google’s SGE is essentially pulling from its index – if your page isn’t indexed or is very slow (perhaps causing live crawlers to skip it), you’re out. Ensure your content is readily indexed by search engines (fetch as Google, etc., to verify). Also, Bing’s index plays a big role in ChatGPT/Bing – so use Bing Webmaster Tools to make sure your site is indexed there too.
  • Consistency & Accuracy in Data: Technical SEO includes ensuring consistent information across your site and platforms. If your hours, prices, or other facts differ between your site, Google Business Profile, and Wikidata, AI might get confused or deem the info less credible. One philosophy for AI-era SEO is “single source of truth” – maintain a well-organized, updated repository of facts (even if it’s a page on your site with all key facts), and mark it up. This reduces the chance of AI grabbing outdated or incorrect info from elsewhere.

Think of this pillar as making your site AI-friendly. Just as mobile-friendly sites gained favor when mobile search rose, now we need AI-friendly sites: technically sound, semantically structured, and transparent to machine readers.

3. Content & Context Strategy (Optimize for Answers, Not Just Clicks)

Content is where SEO and GEO intersect most deeply. Under this pillar:

  • Direct Answer Content: Include content on your site that directly answers common questions in your domain. This means having a robust FAQ section, how-to guides, comparisons, etc. If there are “People Also Ask” questions relevant to you, make sure you have content that answers each of them in a clear, concise way (one question per FAQ item, for example).
  • Conversational Tone (where appropriate): While maintaining professionalism, content can be written in a slightly more conversational style if it fits your brand. This helps AI use your text in its answers naturally. For instance, a heading that says “How do I choose the right insurance plan?” followed by a straightforward answer will slip nicely into an AI’s response if that question is asked.
  • Depth and Breadth: Cover the topic comprehensively. In SEO, we talk about covering subtopics to rank well. Similarly, an AI is more likely to trust and use content that it “thinks” covers all the bases. If your article on electric cars mentions range, charging, cost, and safety, and a competitor’s article only covers range and cost, your content is more likely to be used for a question about safety or charging.
  • Contextual Embedding: Provide context that ties your brand to key themes. For example, if you are a sustainable fashion brand, ensure your content frequently and naturally associates your brand name with “sustainable fashion” and related concepts. LLMs work on patterns – the more it sees “Brand X – sustainable fashion”, the more likely it will include Brand X when asked about sustainable fashion brands. This is about influencing the AI’s internal associations (without spamming, of course).
  • E-E-A-T for Content: Emphasize Experience, Expertise, Authority, and Trustworthiness in content. Not just for Google’s sake, but because AIs trained on web content have effectively ingested those signals. If all top sources on a topic cite a statistic and you’re the only one who doesn’t, the AI might consider your content less complete. If you have expert quotes or first-person insights (“As a dermatologist, I recommend…”), the AI may pick that up as valuable nuance.
  • Content Assets (beyond text): While text is king for LLMs, don’t ignore images, charts, and videos. Google’s AI is already showing images in answers, and YouTube video citations in SGE increased 300% after August. Create supporting visuals for your content – diagrams, infographics, videos – and optimize their metadata (titles, alt text). These could either be referenced by an AI (e.g., “according to the chart from [Brand]…”) or actually displayed (in AI search UIs that show images). Brands should consider developing visual or video content for topics where a demonstration or visualization aids understanding. The presence of the “Watch on YouTube” thumbnail in an AI answer highlights that content strategy for GEO isn’t text-only. If your content can be the one featured as a video or image, you capture that query’s attention in a multimedia way.

The overarching philosophy for content in GEO is be the best answer, not just the best result. Instead of thinking “how do I make them click my link?”, think “how do I ensure the answer (even if it’s read out loud by an AI) includes my insight or brand?” This might feel counterintuitive to those used to optimizing for clicks, but in the answer engine world, delivering value within the answer is key to brand exposure.

4. Brand Presence & Authority (Influence the AI’s Knowledge Graph)

This pillar is about your brand’s footprint beyond your site:

  • Off-site Mentions and Citations: Cultivate mentions of your brand on high-authority sites and data sources. Press releases in major outlets, expert quotes in articles, inclusion in industry reports – these all feed the AI’s training data or real-time search results. If an AI is answering “top fintech companies”, and your brand has been cited by CNBC, mentioned on Wikipedia, and included in a Gartner report, you stand a much better chance than if your presence is only on your own blog.
  • Owned Asset Optimization (OAO): Terakeet calls this Owned Asset Optimization – ensuring all your owned content (website, social profiles, knowledge panel info, etc.) is telling a coherent, positive story that AI can pick. That means keep your social media updated (AIs like Bard have been known to pull info from Twitter or LinkedIn for certain queries), maintain your Google Business Profile (Bing and Google’s AIs use that for local queries), and even consider maintaining a Wikipedia page (if guidelines allow) or Wikidata entries for key facts.
  • Partnering with Trusted Entities: This is more nebulous, but if you can collaborate with universities, standards organizations, or similar, such that your content or data is cross-published or referenced there, it boosts the likelihood of AI trust. For example, if an AI recalls a statistic, it might prefer “According to a study by [University] in partnership with [Your Company]” over just “[Your Company] says…”. It’s about adding layers of credibility.
  • Active Engagement in Q&A Spaces: Although AI might overshadow platforms like Quora or Stack Exchange in the future, those platforms are also training data. If an expert from your company provides high-quality answers on Stack Exchange or Reddit that get upvoted, those might influence the AI’s training data or even be directly quoted. This happened in early ChatGPT where it would sometimes regurgitate a highly upvoted answer from StackOverflow (with no attribution). While this is hard to scale, it’s a tactic for subject-matter experts to seed correct information in the public sphere.
  • Brand Signals in Training Data: This is forward-looking, but consider making certain data open or easy to crawl. Some companies release research or guides under Creative Commons, which then get picked up by Wikipedia or cited widely. That means by the time an LLM is trained, that company’s insights are “baked in” to the model’s knowledge. We can think of this as a modern spin on link building – instead of links for PageRank, you want references in widely-consumed content for LLM rank.

Overall, this pillar’s philosophy is influence through authority. In SEO we often say “be the authority that others cite, and Google will reward you.” In GEO, the “others” include AI systems themselves. They “think” in terms of what content is credible based on patterns and citations in their training. Building your brand’s authority across the web not only helps SEO; it literally trains the next generation of AI to regard your brand as an entity worth mentioning.

5. Monitoring & Adaptation (Continuous Improvement Cycle)

No framework is complete without measurement and adjustment. As discussed in the Tools article, monitoring is challenging but essential. Under this pillar:

  • Set GEO KPIs: Define what success looks like for you. It could be “appear in AI answers for 50% of top 20 questions in our category” or “achieve citations/mentions in AI results equal to our market share.” It might also be indirect metrics like traffic from Bing Chat (via the “click for more” citations) or an increase in branded search volume (if AI mention drives awareness). Having KPIs will help justify GEO efforts to stakeholders.
  • Regular Auditing: Make it a routine (monthly or quarterly) to audit AI answers for a set list of queries. Document if your brand is mentioned, how the answer is phrased, and what sources are cited. Track this over time. If you implement changes (new content, press push, etc.), see if there’s a corresponding change in these audits.
  • Leverage Feedback: Use feedback mechanisms – some AI platforms allow users to thumbs-up/down an answer or suggest an edit. If an AI provides an answer about your brand that’s wrong or subpar, use that feedback. On the flip side, if it gives a great answer featuring you, upvote it or save it as a positive example.
  • Stay Agile: The AI landscape can change with a single model update. (We’ve seen instances where a model update suddenly changed which sources it prefers or how it formats answers.) Be prepared to adjust. This could mean updating your content if the AI starts quoting a specific line (maybe to ensure it’s perfectly phrased), or it could mean refocusing efforts if, say, Google’s AI starts preferring content updated within 3 months (you’d need to refresh content more often).
  • Cross-Team Collaboration: GEO isn’t just an “SEO team” thing. It may involve PR (for off-site mentions), social media (for feeding info into public discourse), engineering (for technical tweaks and data feeds), and leadership (for partnerships and big-picture moves). Set up a periodic review with all these stakeholders to share what you’re learning about how the AI “sees” your brand. This ensures company-wide alignment. For example, if the AI is consistently mentioning a competitor’s eco-friendliness, that’s a signal to your product and comms teams about a narrative you might be missing.

The philosophy here is continuous improvement. Just like SEO is not one-and-done, GEO will be iterative. We might even consider a new role (as we’ll discuss in the Career Advice article) – someone who owns monitoring AI and driving those insights back into strategy. Incorporating GEO metrics into your regular marketing dashboards will keep it from being a novelty and make it a core part of your strategy.

Philosophies to Guide GEO Strategy

Beyond the tactical framework, it’s important to have guiding principles for GEO work:

  • User-Centricity Remains Key: Just as Google’s algorithms ultimately chase user satisfaction, AI answers are aimed at satisfying user queries. If you keep the end-user’s needs at the center (providing clear, helpful answers and resources), you’ll naturally align with what the AI is trying to do. Avoid the temptation to overly “gamify” the AI – if something feels like it’s only there to trick the AI, it probably won’t hold up long-term (AI companies are actively training models to avoid manipulative content).
  • Transparency and Accuracy: In an era of misinformation concerns, brands that are transparent and factual will earn trust – not just with users, but with the AI models that are tuned to detect signals of trustworthiness. If you make a claim, back it up. If you have an expertise, highlight credentials. One philosophy is “Always aim to be the cited source.” If an AI were to provide a citation for a statement, you want it to be your site because you presented that info so well and credibly.
  • Influence, Don’t Control: A mental shift for GEO is accepting that you often can’t directly control the outcome (the AI’s answer), but you can influence it. This is similar to how PR works – you can’t control what the media writes, but you can provide the story and facts to steer it. GEO is more about influence than outright control. This mindset will help you be patient and creative in your strategies (as opposed to trying to find one hack that forces your link into every answer – that’s not realistic).
  • Ethical SEO/GEO Alignment: Ensure your GEO tactics align with your brand’s ethics and quality standards. For example, don’t spam forums or create low-quality content hoping to feed the AI; that could backfire by associating your brand with junk. Instead, focus on genuine value. Not only is that better for the world, but it’s increasingly what AI models are trained to promote. This also includes respecting user privacy – for instance, if using conversational data to tailor content, do it in a way that respects anonymity and data protection. (AI search is raising new questions about privacy, so brands should be mindful.)
  • Holistic Integration: GEO shouldn’t be a silo. Integrate it with SEO and overall marketing. We’re already seeing the term “Search Everywhere Optimization” – the idea that you must optimize for all the places people search (Google, Bing, Amazon, YouTube, ChatGPT, voice assistants, etc​). The philosophy here is to break down the silos between “SEO”, “app store optimization”, “social media”, and see it as one holistic effort to make your brand discoverable. AI is blurring the lines between these channels (e.g., an AI might pull info from social media to answer a search query). So our strategies must be holistic too.

To tie it all together, think of GEO framework as an extension of your SEO framework:

  • You Research what users ask and how AIs respond (much like keyword & SERP research).
  • You fix Technical issues to ensure crawlability and data clarity (much like technical SEO).
  • You create and optimize Content that addresses queries directly (on-page SEO + new content formats).
  • You build Authority through off-site presence and aligning your brand with trust signals (off-page SEO, digital PR).
  • You Monitor results and Adapt (analytics and iterative optimization).

This mirrors the SEO process we know, but applied to the AI context. The ingredients of GEO success include things like research, content strategy, and brand authori​ty. – which are familiar, yet require new tactics.

By adopting this framework and mindset, you’ll be well-equipped to navigate the changing search landscape. You’ll move from a reactive stance (“AI is messing up my traffic!”) to a proactive one (“AI is another channel I can optimize for, with the following plan…”).

In the next article, we will specifically address the ethical considerations and biases in GEO – essentially diving deeper into that guiding philosophy of doing GEO in a responsible way, both for your brand’s reputation and for broader fairness.