AI engines do not see your site the way a person does. They see the code, before the design, often before the JavaScript even runs. The Technical AEO Audit reads your site the way those engines do, finds exactly what is blocking them, and hands you the precise fixes to make every page easy to read, trust, and cite.
No credit card required. Your readiness score in about a minute.
A page that looks full and polished in a browser can arrive nearly empty to a crawler. The content loads with JavaScript the bot never runs, the schema is missing or contradicts the page, there is no llms.txt to guide the models, and key facts sit inside images. None of this is visible when you look at the page yourself, which is exactly why it goes unfixed for months.
You published the definitive guide. It renders on the client, so the crawler receives an almost empty shell. The work is done and the machine cannot see it.
Being crawlable for Google is not the same as being parseable and citable by an LLM. There are new expectations, new standards like llms.txt, and a heavier reliance on clean structured data.
Technical issues do not announce themselves. A marketer cannot see malformed schema or a blocked path. The content just quietly fails to get cited, and nobody can say why.
Read your site the way AI engines do, find exactly what is blocking them, and turn it into the precise fixes that make you easy to cite.
The audit does not guess from the outside. It fetches and renders your pages the way AI engines do, checks the things that decide whether a machine can read and trust them, scores the result, and turns every issue into a specific fix. Here is the path.
Fetch your pages the way AI engines do.
Run them the same way, and see what a machine receives.
Validate schema, llms.txt, crawlability and structure.
Rate how readable and citable each page is.
Turn each issue into a ranked, specific fix.
A live site in. A clear readiness score and an ordered list of fixes out.
The trap is judging a site by how it looks in a browser. An AI crawler does not see your design, it sees the code, and increasingly it reads what is there before any JavaScript runs. So we fetch and render your pages the way these engines do, then compare what a machine actually receives against what a visitor sees. When a page that looks full to you arrives nearly empty to a bot, that gap is the problem, and it is invisible from the outside.
Every check is validated, not just detected. A schema block that exists but contradicts the visible content is worse than none at all, because engines learn to distrust the page. So we confirm that what the code claims and what the page shows actually agree, rather than ticking a box because a tag is present.
This is the gap that catches almost everyone. Your page looks complete to you because your browser runs all the code that fills it in. Many AI crawlers do not, or they read the raw response first. If your content arrives only after the JavaScript runs, the machine may see a shell where you see an article.
We show you both views side by side, so a problem that is invisible in a browser becomes obvious in a glance.
Readiness is not one thing, so the audit looks at the layers that together decide whether an engine can read a page, trust it, and quote it cleanly.
Structured data and schema, so the model knows what a page is and can lift facts without guessing. llms.txt, the emerging file that tells AI systems what your site is and what matters most. Crawlability, the robots rules, sitemaps and redirects that decide what gets seen at all. And page structure, the clear headings and direct answers that make content easy to extract.
A long list of red flags is just more work. So the audit does not stop at finding problems. Every issue is turned into a specific fix, weighed by how much it improves AI readability and how hard it is to do, and handed to the Recommendation Engine to rank against everything else competing for your time.
What you get is short and ordered: the few changes that will most improve how easily AI can read and cite you, first.
| A traditional SEO audit | Technical AEO Audit |
|---|---|
| Crawls like a search engine of old | Reads your site the way AI engines do |
| Misses JavaScript-only content gaps | Compares raw and rendered to expose them |
| Detects schema if a tag exists | Validates schema against the visible page |
| Has never heard of llms.txt | Checks llms.txt presence, format and coverage |
| Hands you a long list of warnings | Hands you a few ranked fixes |
Two moments where knowing what the machine sees changes the outcome.
The audit usually finds the reason in the plumbing.
Re-auditing confirms a fix worked before you move on.
"GrackerAI helped us reach developers and decision-makers right when they were researching. The pipeline impact has been remarkable."
Most teams pour effort into content and never check whether AI can actually read it. The audit closes that gap, turning silent technical failures into a short, ranked list of fixes that make every page you publish easier to cite.
Your technical fixes land here, ranked against every other opportunity by impact, so the most important changes happen first.
Pair a readable site with content built to be cited, with the structure and schema AI engines reward.
Technical readiness is one input into how citable you are. Watch your visibility score respond as you fix it.
Discover your AI visibility gaps and start capturing millions of new product discovery clicks.
