Learn Technical SEO for AI Search

Technical SEO Audit Template & Free Training Course

What is Technical SEO?

Technical SEO is the practice of ensuring that search engines can discover, crawl, render, understand, and index your website efficiently. It focuses on the underlying infrastructure and systems that support your content, rather than the content itself.

At its core, technical SEO is about removing friction, making it as easy as possible for search engines to access your site and interpret what each page is about. If search systems can’t reliably crawl or understand your pages, even the best content will struggle to perform.

What technical SEO covers

Technical SEO typically includes areas such as:

Site architecture and internal linking:
How pages are organized, connected, and prioritized across your site.

Crawlability and indexability:
Whether search engines are allowed to access your pages and whether those pages can be indexed correctly.

Rendering and JavaScript handling:
Ensuring that important content is visible to search systems, even on sites that rely heavily on JavaScript.

Performance and page speed:
Fast, stable sites improve both user experience and search engine efficiency.

Structured data and metadata:
Using schema, canonical tags, and other signals to clarify meaning and relationships between pages and entities.

Status codes and error handling:
Managing redirects, broken pages, and server errors so search engines don’t waste crawl resources.

What Does Search Look Like Today?

Modern search engines increasingly integrate artificial intelligence (AI) systems into their processes. 

From Google’s AI Overviews to conversational assistants like ChatGPT, Perplexity, Claude, and others, these AI-driven search systems do more than rank “blue links”. Instead, they synthesize answers from multiple sources and present a response directly to the user. (Enter the Query Fan, the new SEO buzzword on the block.)

This changes how website content is delivered to and consumed by users online. 

Why technical SEO still matters for AI-assisted search

Technical SEO has always been foundational to search performance—and that hasn’t changed. 

Modern search systems increasingly rely on context, structure, and clarity to interpret content. So if a page loads slowly, renders incompletely, or hides key information behind inaccessible code, search engines may only see a partial or distorted version of that page.

In practical terms, strong technical SEO:

• Helps AI-assisted search systems fully access your content

• Improves consistency and reliability across large sites

• Supports accurate interpretation of page meaning

• Reduces the risk of visibility loss caused by crawl or rendering issues

You might hear on LinkedIn: “SEO is dead, long live GEO (Generative Engine Optimization).” But this just ain’t true! The reality is that the value of SEO is alive and well. 

How (AI) Search Works Today

Modern AI search systems blend traditional retrieval with AI-assisted synthesis. They don’t just match keywords; they evaluate meaning, context, and relevance to generate responses. This shift places new emphasis on how content can be understood by machine models, not just indexed and ranked.

Check out this AMA with Emina Demiri who demystifies AI search, debunks bogus tactics, and recommends her go-to tools.

Passage-level retrieval

AI models increasingly retrieve passages of text rather than whole pages. This means that search engines are looking for discrete units of content (often small sections or paragraphs) that directly answer user queries with precise information.

Semantic and contextual understanding

Where traditional SEO prioritizes keywords and links, AI systems add semantic layers. They interpret relationships between concepts and entities, turning text into meaningful information that can be composed into answers.

Citation and inclusion patterns

AI-generated responses often cite content directly. Being part of that citation pool, and not just ranking on page 1 of Google, becomes a form of visibility in itself. This can affect how users discover brands, even if they never click through to a site.

The Continued Importance of Technical SEO Fundamentals

Even as AI influences search, nothing replaces the basics. As iPullRank’s Zach Chahalis says himself during our Advanced SEO for AI training course, “core technical SEO hygiene is a tale as old as time.”

In other words, good technical SEO remains the foundation on which visibility in both traditional and AI-driven search is built.

Technical accessibility

If AI crawlers can’t access your content, they can’t interpret it. This means ensuring:

• Clean site architecture and internal linking for discoverability

• Clear rendering paths so that bots see what users see

• No unintentional blocks in robots.txt or via JavaScript-only navigation

These fundamentals are no different than what you’d optimize for traditional search. They just now also feed into AI systems’ understanding.

Structured data and metadata

Structured data (such as JSON-LD schema) helps search systems contextualize entities and relationships on a page. While AI models use more than structured data, having semantic markup gives them clearer signals about what your content is about. This helps both traditional ranking and contextual interpretation.

Performance and rendering

AI systems often have limited time and computational budget to crawl and render content. Fast performance and fully-rendered accessible HTML improve the likelihood that content is fully seen and understood.

The Customer Journey Has Changed

The use of AI for search and discovery is growing rapidly:

• Google AI Overviews have become commonplace, with approximately 2 billion monthly users 

• Almost 60% of organizations are integrating generative AI into their marketing efforts, from minimal to no adoption the previous year

• Of 250 top digital leaders, 94% plan to increase their GEO investments in 2026

• The adoption of AI is impacting SEO teams most notably by restructuring its roles and responsibilities

These trends underscore that while traditional search remains essential, AI search behaviours cannot be ignored.

Besides the technical nuances of how AI systems access and retrieve information, a major change that has come with the introduction of large language model (LLM) assistants is to the buying journey.

If we think about the traditional funnel of awareness, consideration, and decision, prior to AI search, the buyer would usually be interacting with your website throughout these stages. 

Increasingly now however, the customer is becoming aware of your brand, researching your brand, and even making their purchase decision based purely on LLM responses. They might never actually visit your website, as CEO of Waikay Dixon Jones highlights in this clip below:

This shift emphasizes how important branded search queries are in today’s AI-powered landscape, as buyers access your website only after their purchase decision has already been made. 

It may also herald the start of what experts are calling “The Agentic Internet” and a future in which humans don’t access websites at all, their AI assistants do for them. So while it’s still essential to create content for the user, it’s becoming even more important to ensure that content is accessed and understood by bots.

Practical SEO Strategies for the AI Search Era

1. Structure content for meaning and extraction

Break content into clearly labelled sections with precise answers near the top of each section. This helps both algorithmic systems and users find relevant information quickly.

2. Use clear, consistent entities

Consistent use of entity names and terms (like product names, concepts, and proper nouns) helps search systems link ideas correctly. Tools like schema can help reinforce these signals.

3. Maintain technical hygiene

Ensure bots can crawl, render, and index pages effectively. Audit for JavaScript rendering and any rules in robots.txt that could block access.

4. Aim for high evidence density

Content that is factual, well-referenced, and specific is more likely to be used as a source for AI responses. Include statistics, references, and properly structured lists where relevant.

5. Track both traditional and AI visibility

Continue monitoring search rankings and traffic, but consider tracking where and how content is cited or referenced in AI-driven systems using tools like DemandSphere and Waikay.

6. Get access to your log files

Server logs show you exactly which bot, how often and what pages are being requested, demystifying any assumptions on crawl budget.

Psst! Paula Gonzalez calmly explains 3 SEO pillars you should cling to as AI takes over search, as well as provides some best practice checks you can look into today.

This is SEO Evolution, Not Replacement

The rise of AI search platforms reflects a broader evolution in how people discover information. While AI introduces new considerations into SEO, it does not invalidate the fundamentals. 

Strong technical SEO, clear content structure, and accessible information remain essential. The AI landscape adds an important lens that emphasises interpretation and relevance as users increasingly expect direct, context-rich answers.

By adopting an SEO strategy that meets both traditional ranking signals and modern AI search expectations, brands can remain visible, authoritative, and discoverable in the evolving search ecosystem.