As more people turn to AI tools for everyday solutions and these models change how we find information online, it is increasingly important that websites are built in ways that both people and machines can understand.
Did you know that AI reads websites a lot like a screen reader? This assistive software is used by a wide range of users — including those who are blind, have low vision, or process information better audibly — meaning AI relies entirely on clean digital structure rather than visual design. If your code is messy or if content is hidden behind rigid login walls, client-side rendering bottlenecks, or inaccessible PDFs, an AI might never find your site, which means your content won’t show up in AI-generated answers.
Here is how we ensure web content is accessible for both humans and AI:
1. Prioritize Structure Over Code Tricks
ARIA labels (special code snippets added to HTML to help assistive technologies understand a page) can be helpful, but overengineering them often creates more problems than they solve.
What we do: We prioritize semantic HTML first. This means using HTML elements for their intended purpose, such as <button> for buttons rather than styled <div> containers, <nav> for main navigation, and real <table> loops for tabular data grids. We ensure headings follow a strict, logical hierarchy (H1 → H2 → H3) hardcoded into CMS templates. If the structure makes sense to a screen reader, it will make perfect sense to an AI crawler.
2. Choose Clear Over Clever
Clear, straightforward, and consistent language matters more than ever. AI models easily get tripped up by vague corporate marketing speak, dense jargon, or institutional fluff, which can ultimately keep your content from surfacing in AI-generated answers.
What we do: We ensure our content strategy focuses on transparent terminology and task-centric information architectures designed to answer users’ questions directly. We summarize the core answer within the first 200 words of a page block. From there, our writers frame deeper technical specifics or programmatic outcomes using concise text that scans beautifully. We keep terms consistent across every page so neither humans nor machines get confused.
3. Unlock Hidden Content & Improve Speed
Critical information is often trapped inside attachments or buried behind slow-loading pages, which frustrates human visitors and blocks AI crawlers entirely.
What we do: We extract key data from locked PDFs and bring it onto real, indexable web pages. We also treat performance as a core metric. Many proprietary LLM crawlers ingest initial HTML delivered directly by your server without executing heavy client-side JavaScript. A page that loads too slowly is effectively unreachable for mobile users and likely to be skipped by AI models altogether.
4. Strategize Where We Start
Fixing an entire website at once can feel overwhelming, so we are intentional about our focus.
What we do:
We prioritize pages and content directly tied to key business and user goals first. By focusing our energy on the areas that matter most to our clients and their user base, we can make the biggest immediate impact on both user experience and AI discoverability.
Don’t Reinvent the Wheel, Build it Correctly
The foundation of great SEO and AI discoverability is still just a great user experience.
Strong semantic structure, rich microdata schema markup (like JSON-LD), fast platform performance, and accessible experiences have always been the bedrock of good digital work, and that hasn’t changed. Rather than chasing temporary “AI tricks” or trying to game neural networks, teams are better served by getting these fundamentals right. We don’t need to reinvent the wheel for the AI age; we just need to build the wheel correctly.
The better a website works for humans, the better it works for machines.
About Rachel Huber
Rachel brings warmth, organization, and an eye for clear writing to her work as a content guru. She supports content strategy and development across phases—helping teams shape language that’s structured, purposeful, and easy to maintain.
Posted on July 27, 2026 by Rachel Huber in Commentary, News
As more people turn to AI tools for everyday solutions and these models change how we find information online, it is increasingly important that websites are built in ways that both people and machines can understand.
Did you know that AI reads websites a lot like a screen reader? This assistive software is used by a wide range of users — including those who are blind, have low vision, or process information better audibly — meaning AI relies entirely on clean digital structure rather than visual design. If your code is messy or if content is hidden behind rigid login walls, client-side rendering bottlenecks, or inaccessible PDFs, an AI might never find your site, which means your content won’t show up in AI-generated answers.
Here is how we ensure web content is accessible for both humans and AI:
1. Prioritize Structure Over Code Tricks
ARIA labels (special code snippets added to HTML to help assistive technologies understand a page) can be helpful, but overengineering them often creates more problems than they solve.
What we do: We prioritize semantic HTML first. This means using HTML elements for their intended purpose, such as <button> for buttons rather than styled <div> containers, <nav> for main navigation, and real <table> loops for tabular data grids. We ensure headings follow a strict, logical hierarchy (H1 → H2 → H3) hardcoded into CMS templates. If the structure makes sense to a screen reader, it will make perfect sense to an AI crawler.
2. Choose Clear Over Clever
Clear, straightforward, and consistent language matters more than ever. AI models easily get tripped up by vague corporate marketing speak, dense jargon, or institutional fluff, which can ultimately keep your content from surfacing in AI-generated answers.
What we do: We ensure our content strategy focuses on transparent terminology and task-centric information architectures designed to answer users’ questions directly. We summarize the core answer within the first 200 words of a page block. From there, our writers frame deeper technical specifics or programmatic outcomes using concise text that scans beautifully. We keep terms consistent across every page so neither humans nor machines get confused.
3. Unlock Hidden Content & Improve Speed
Critical information is often trapped inside attachments or buried behind slow-loading pages, which frustrates human visitors and blocks AI crawlers entirely.
What we do: We extract key data from locked PDFs and bring it onto real, indexable web pages. We also treat performance as a core metric. Many proprietary LLM crawlers ingest initial HTML delivered directly by your server without executing heavy client-side JavaScript. A page that loads too slowly is effectively unreachable for mobile users and likely to be skipped by AI models altogether.
4. Strategize Where We Start
Fixing an entire website at once can feel overwhelming, so we are intentional about our focus.
What we do:
We prioritize pages and content directly tied to key business and user goals first. By focusing our energy on the areas that matter most to our clients and their user base, we can make the biggest immediate impact on both user experience and AI discoverability.
Don’t Reinvent the Wheel, Build it Correctly
The foundation of great SEO and AI discoverability is still just a great user experience.
Strong semantic structure, rich microdata schema markup (like JSON-LD), fast platform performance, and accessible experiences have always been the bedrock of good digital work, and that hasn’t changed. Rather than chasing temporary “AI tricks” or trying to game neural networks, teams are better served by getting these fundamentals right. We don’t need to reinvent the wheel for the AI age; we just need to build the wheel correctly.
The better a website works for humans, the better it works for machines.
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About Rachel Huber
Rachel brings warmth, organization, and an eye for clear writing to her work as a content guru. She supports content strategy and development across phases—helping teams shape language that’s structured, purposeful, and easy to maintain.