Beyond SEO: Why Core Web Vitals, GEO, and AEO are the Pillars of Your New LLM Optimization Strategy

Summary: Current SEO practices, split between technical, content, and local teams, are obsolete. The rise of AI-driven search, including Google’s SGE, demands a unified approach. Success now hinges on integrating Core Web Vitals (CWV), geographic context (GEO), and Answer Engine Optimization (AEO) into a single LLM Optimization framework. This article argues that moving from “keyword optimization” to “intent fulfillment” is the only viable path forward for enterprises in Toronto and across Canada.

Your current SEO roadmap is broken. The technical backlog, the content calendar, and the local SEO checklist are failing to address the single greatest shift in search history: the move from a list of blue links to a direct, synthesized answer.

As digital leaders, we have spent a decade mastering the optimization of keywords. We built specialized teams to handle technical performance, content creation, and local listings. We treated these as separate pillars of a successful strategy. This model is now failing.

AI-driven search engines and Large Language Models (LLMs) are not just “searching” your site. They are ingesting it. They are evaluating your entire domain as a whole to determine if it is a trustworthy, high-quality source from which to synthesize a direct answer for a user. The era of the “ten blue links” is ending, and the era of the “direct answer” is here.

Surviving this fundamental shift requires a pivot from “keyword optimization” to “intent fulfillment.” This demands a new, Holistic SEO strategy where technical performance, local context, and answer-first content are unified. This is the foundation of LLM Optimization.

LLMs Don’t Crawl, They Ingest: The New Value of Content

We must stop thinking about search engines “crawling” and “indexing” individual pages. LLMs ingest and validate concepts and entities across your entire site.

AI models value content differently than traditional search algorithms. They are not looking for keyword density or a specific number of backlinks. They are looking for signals of trust and utility that allow them to confidently present your information as fact.

This new validation process relies on three components:

1. Structured Data (Schema): Schema markup (like FAQPage, LocalBusiness, Article) is no longer a “nice to have” for rich snippets. It is the explicit instruction manual you give the AI. It tells the LLM what your content is, who wrote it, where it applies, and how to use it. A page without schema is an unstructured, ambiguous text file to an AI.
2. E-E-A-T Signals: Expertise, Experience, Authoritativeness, and Trustworthiness are the LLM’s primary validation metrics. The model cross-references your authors, your brand mentions, and your factual claims against other trusted sources in its training data. Clear author bios, verifiable citations, and factually dense content are no longer optional.
3. Answer Engine Optimization (AEO): Content must be formatted to answer questions directly. LLMs are designed to synthesize information, not just pass a user to another link. Your content must be the most citable, clear, and direct answer to a user’s query. This means formatting content with “What is,” “How to,” and “Why” headings, using definitive language, and structuring information for quick extraction.

If your content is not structured for ingestion, the LLM will ignore it and synthesize an answer from a competitor who has done this work.

 Core Web Vitals and AI: Why Performance is the New Trust

For years, Core Web Vitals (CWV) were treated as a technical SEO chore. In-house teams often viewed it as a tie-breaker metric for Google rankings, managed exclusively by the development team.

In the AI era, CWV is a foundational signal of quality and trust.

Think of it from the model’s perspective. A site with poor CWV, a slow Largest Contentful Paint (LCP), high Cumulative Layout Shift (CLS), or poor Interaction to Next Paint (INP), provides a frustrating and unreliable user experience. AI models are trained to associate poor user experience with low-quality or untrustworthy content. A user who “bounces” back from a slow page is sending a direct, negative quality signal that AI registers.

There is a second, more mechanical reason. Ingesting and processing the web is resource-intensive. An LLM ingestor, like any crawler, operates on a budget. A slow, heavy, unstable site is expensive to process. It is logical that these sites will be processed less frequently or less deeply than fast, efficient ones.

A technically sound, fast, and stable site is the foundation for being a trusted source. Technical performance is the price of entry for LLM Optimization. A poor CWV score is a signal of low quality, making your content less likely to be chosen as a citable source for an AI-generated answer.

Context is King: Unifying GEO and Answer Engine Optimization

Ambiguous queries are the norm in search. A user in downtown Toronto searching for “best tacos” is not on a general research mission; they are looking for dinner now.

Traditional SEO optimized for the keyword “best tacos Toronto.” An AI Search Strategy optimizes for the intent: “What is the best, highest-rated taco restaurant open now near my current location?”

This is where geographic optimization (GEO) and Answer Engine Optimization (AEO) must converge. They provide the critical context LLMs need to fulfill user intent.

• GEO Provides the “Where”: Your LocalBusiness schema, your Google Business Profile signals, and your location-specific landing pages provide the explicit local context. The AI uses this data to understand the query’s implied “near me” context, even when the user doesn’t type it. A Toronto-based business must have this explicit local data to be considered for a locally-intentful query.
• AEO Provides the “What” and “Why”: Your on-page content must be structured to answer the implied questions: “What tacos do they have?” “What are the hours?” “Is it highly rated?” “What is the price?”

An LLM synthesizes these signals instantly. It combines the user’s location (GEO data) with your site’s AEO-structured content (“Our award-winning al pastor tacos are available for C$7 at our Queen West location, open until 11 PM”) to generate a direct, actionable answer.

If your local signals are weak, or your content is buried in a long, narrative paragraph, you will not be the source for that answer.

The Flaw in Our Thinking: “This is Too Complex”

The most common objection I hear from in-house SEO managers is that this sounds overwhelmingly complex. Teams are already stretched thin managing technical backlogs, content production, and local SEO. How can they possibly add another layer?

This perspective views these tasks as separate. That is the old, failing model.

This new, unified framework is not more work; it is smarter work. Integrating these pillars creates compounding returns. The siloed approach, by contrast, creates waste.

Consider the old, inefficient model:

1. Your content team writes a 2,000-word blog post.
2. Three months later, your technical team tries to optimize its CWV scores after it’s already built on a heavy template.
3. Your local SEO team isn’t even aware the post exists and never connects it to the relevant Google Business Profile locations.

This is three separate, disjointed efforts resulting in a single, underperforming asset.

Now, consider the unified AI Search Strategy:

1. The strategist identifies an “intent” to capture (e.g., “AI-ready business phone systems in Toronto”).
2. The technical team ensures the page template achieves a 95+ PageSpeed score before any content is written.
3. The content team writes the page in AEO format (Q&A, feature tables, clear definitions) and embeds LocalBusiness schema.
4. The local team ensures the new page is linked from the Toronto Google Business Profile as a primary service.

This is one asset, created through one unified workflow, that now serves traditional search, local search, and AI-driven search simultaneously. This Holistic SEO approach is more efficient, not more resource-intensive.

Your Next Step: From Siloed Audits to an AI Readiness Audit

The first step is to stop measuring success in silos. Your technical SEO team’s CWV report and your content team’s keyword-ranking report are missing the complete picture.

You must benchmark your unified presence against your competitors. How does your site perform when all factors are combined? Where are the specific gaps where an LLM will choose a competitor’s content over yours?

Stop auditing your site in pieces. Schedule a unified “AI Readiness Audit.”

This audit must be a cross-functional analysis. It must benchmark your technical performance (CWV) against your content’s answer-readiness (AEO) and your local signal accuracy (GEO). This is the only way to identify the exact gaps, prioritize your efforts, and build a strategy that wins not just today’s search results, but tomorrow’s AI-generated answers.