One size doesn’t fit all: optimizing for AI search across different LLMs

Presented @ BrightonSEO San Diego in 2025.

Here’s the harsh truth: There is no universal AI search optimization strategy.

Why? Because different large language models (LLMs) — like Google AI Overviews, ChatGPT, and Perplexity — are trained on different data, use different methodologies, and generate results in different ways.

If you’re treating them all the same, you’re leaving visibility (and results) on the table.

In this session, we’ll break down:

• How major AI models differ in training data, ranking factors, and content retrieval.

• How to tailor your content to increase the chances of being cited, summarized, and surfaced across different AI-driven platforms.

• Actionable GEO (generative engine optimization) strategies for Google, OpenAI, and emerging AI engines.

• What brands and marketers need to do now to avoid becoming irrelevant in an AI-first search landscape.

Transcript

1. Optimizing for AI Search Across LLMs Sam Richardson Intero Digital speakerdeck.com/srichardson /samuelwrichardson One Size Doesn’t Fit All:
2. Sam is the vice president of growth at Intero Digital, a digital marketing agency with an emphasis on SEO and paid search.
3. the search game has changed
4. Generative engines don’t rank; they interpret. Generative AI
5. Generative engines don’t rank; they interpret. Users aren’t clicking links; they’re consuming answers. Generative AI
6. Users aren’t clicking links, they’re consuming answers. GEO is about influencing architecture. Generative AI
7. SEO is about rankability. GEO is about retrievability.
8. Why Rankings Alone Don’t Equal Visibility
9. LLMs Don’t Rank. They Interpret.
10. Visibility is determined by what’s been learned and what’s available.
11. Brand mentions, structured data, and content format matter more than ever.
12. Not All Search Platforms Work the Same
13. Layer Purpose Pre-Trained LLM Provides the base knowledge of language patterns and associations
14. Layer Purpose RAG Grounds the LLM's responses in fresh external data to increase accuracy and relevance
15. Layer Purpose Agentic Capabilities Advanced reasoning, multi-step planning, and use of tools
16. You’re not just optimizing for AI, you’re influencing architecture.
17. | How Engines Learn v Entity Associations Models learn which concepts and entities connect Context and Co-occurrence Meaning is shaped by how often and where they appear together Probabilistic Patterns Co-occurrences form statistical patterns captured in embeddings
18. How AI Decides What to Retrieve
19. Embeddings Semantic Search Cosine Similarity Relevant document Query
20. Mind- blowing, right? Stick with me.
21. Influencing AI Search: What Matters at Each Layer
22. Pre-Trained LLM Fixed training data
23. Trained on high-trust sources available during training - public corpora, archived web Pre-Trained LLM Fixed training data
24. Consistent brand and entity mentions Evergreen, entity-rich content Mentions in high-trust, longstanding sources How to Influence Pre-Trained LLM Fixed training data
25. RAG Search-augmented, real-time retrieval
26. Information comes from live external data sources: web indexes, vector databases, knowledge graphs. RAG RAG Search-augmented, real-time retrieval
27. Build topical authority, coverage & depth Structured content formats and schema Entity-level trust and authority How to Influence RAG RAG Search-augmented, real-time retrieval
28. Agentic Capabilities Plans, reasons, and uses tools in multi-step loops
29. Multiple live sources (web search APIs, verticals, structured data, file context) Agentic Capabilities Plans, reasons, and uses tools in multi-step loops
30. Tool-ready data via structured access Build multi-step contextual relevance Establish authority signals for agent trust How to Influence Agentic Capabilities Plans, reasons, and uses tools in multi-step loops
31. | The Retrieval Framework Presence Recognition Accessibility Are you present in AI’s training and retrieval sources? Are you recognized as a relevant and trusted entity? Are your content and the info about your brand accessible to AI?
32. Cooking Up Results for Boos Block
33. 5,665% increase in generative search visibility 189% YoY revenue growth attributed to AI-driven referrals 96.15% increase in conversion rate from AI-powered sessions
34. Retrievability in Action:
35. Steps to Build Your Retrievability Foundation
36. Audit Your AI Presence Understand where your brand actually shows up in AI-generated answers. 1
37. Conduct Entity Research and Identify Semantic Gaps Understand which entities define your space and where you're missing from the conversation. 2
38. Reinforce Entity Presence Across Trusted Sources Build consistency and recognition where AI systems learn. 3 Website
39. Restructure Your Content for AI Retrieval Make your content retrievable, not just rankable. 4 z
40. Inject Your Brand Into Real-Time Signals Influence the layer that relies on fresh data (search-augmented). 5
41. Monitor and Adapt for Generative Visibility Track your retrievability over time, not just traffic. 6
42. Sam Richardson Vice President of Growth, Intero Digital Sam.Richardson@interodigital.com interodigital.com Visit Me at Booth #25 Download the Retrievability Framework