An AI-readiness score is not the point. Fixing it is.

In the last year, checking whether your website is “ready for AI” went from novel to commodity. There are now a dozen free scanners that will take your URL and hand back a grade. A large infrastructure vendor ships one. We ship one too — it’s the free Checker on this site.

So let’s be honest about what a score is worth: not much, on its own.

The score is the easy 5%

We pointed our Checker at 97 well-known B2B SaaS companies — the kind of teams with real budgets and good engineers — and graded each against published, vendor-neutral standards for the three audiences a modern site serves: crawlers, assistants, and buying agents.

84 of the 97 — 87% — scored an F. Not one scored above 53 out of 100. Around 91% gave AI crawlers no specific instructions at all; roughly half had no /llms.txt; none exposed a machine-readable catalogue a buying agent could act on.

Read that again: companies you’ve heard of, almost uniformly failing. Which tells you the diagnosis is not the bottleneck. If nearly everyone scores an F, a tool that simply reports the F is telling you something you could have guessed. The score is the easy 5% of the problem. The hard, valuable 95% is the part every scanner stops short of: actually changing the site.

Why the fix is the hard part

The fixes aren’t exotic. They’re published standards most teams just haven’t gotten to — a parseable /llms.txt, complete Organization and Person schema, an ai-plugin.json and OpenAPI description if you have an API, schema.org Offer markup if you sell something. Nothing here requires inventing technology.

What it requires is engineering time inside your codebase: someone who understands your Astro or Next or Rails app well enough to add the markup to the right templates, wire the build to emit the right files, and not break anything. That’s the work a score can’t do for you. A dashboard that tells you “you’re missing llms.txt” has handed you a ticket, not a solution — and that ticket competes with your roadmap and loses, quarter after quarter. That’s exactly why 86% are still failing.

A score you rent vs. code you own

This is the line that matters when you compare the options. Most of the AI-readiness tools are SaaS dashboards: you pay monthly, you get a number and a checklist, and the implementation is still your problem. The number lives on their domain; the work, if it ever happens, lives on yours.

We do it the other way around. The diagnosis is free — run the Checker, get the grade, no sign-up. The implementation is a fixed-scope engagement that ships as pull requests into your repository. You own the code after. No SaaS layer running on someone else’s infrastructure, no monthly fee to keep seeing your own score, nothing to rip out if you part ways.

A score is something you rent. The fix is something you own. Only one of them changes whether an AI assistant cites you next quarter.

What to do with this

Run the free Checker — get your grade in seconds, same as the 97 sites above. If you land where most do, the number won’t shock you. The useful question is the next one: who’s going to implement the fixes, and where will that code live?

If the answer is “us, eventually, when the roadmap allows,” history says that’s a no. If you’d rather have it shipped into your repo now, against a fixed scope, with a before/after grade to prove it moved — that’s the engagement. Talk to the studio.

The scanners aren’t wrong. They’re just the first 5%.

How we tested: we attempted 122 well-known B2B SaaS companies and successfully scanned 97 (the other 25 block non-browser traffic outright — itself a readiness problem, since several AI crawlers identify as bots). Each site was graded with our light-path Checker against published, vendor-neutral standards — schema.org, llms.txt, OpenAPI 3.1, MCP, ACP, robots.txt and others — scored to 100. No site was named; the numbers are aggregate. Run the same check on your own site at /check.