How do I check if an AI press release platform is really AI-optimized?

Fetch its /llms.txt file (for example, open the platform's domain followed by /llms.txt, or run a curl request against it). A 200 response means the platform publishes machine-readable AI-discovery infrastructure that AI crawlers can actually read. A 404 means the 'AI optimization' has no published, fetchable proof behind it. Repeat for /ai-discovery.json and any AI Discovery Protocol endpoints.

What is the difference between an AI Optimization Score and real AI infrastructure?

An AI Optimization Score is a number a platform assigns to your content. It is only meaningful if there is published, machine-readable proof behind it that AI systems can fetch, such as llms.txt, structured schema, and discovery endpoints. A score with no fetchable infrastructure is a marketing claim, not evidence. Ask to see the endpoints, then fetch them yourself.

What is closed-loop citation verification?

It is the higher standard: after your release is published, the platform queries AI engines like Perplexity and checks whether they actually cite your content, then shows you the citation. Most platforms optimize for citations but never confirm one happened. Closed-loop verification proves the outcome rather than just claiming to influence it.

Can I run these checks on Pressonify?

Yes, and you should. Pressonify's /llms.txt, /ai-discovery.json, and Open Knowledge Format endpoints all return 200 and are publicly fetchable. Pricing is EUR 49.95 per release, EUR 9.95 for your first release. We publish the infrastructure precisely so the claims are checkable rather than taken on trust.

How to Verify an AI Press Release Platform's Citation Claims

Search "AI press release platform" today and every result promises the same thing: get your release cited by ChatGPT, Perplexity, and Google AI Overviews. Some platforms sell it as an "AI Optimization Score." Some call it GEO, or Generative Engine Optimization. The language is everywhere.

Almost none of it is checkable. This post is the test we wish existed when we started: a reproducible way to tell a platform with real machine-readable infrastructure from one that has attached AI language to old-school distribution. It takes about 60 seconds, and you can run every step of it on us.

The 200-vs-404 test

AI systems do not read marketing pages. They read structured, machine-readable files. The single fastest signal of whether a platform is built for AI discovery is whether it publishes those files at all.

Open a platform's domain and add /llms.txt to the end of the URL. Or, from a terminal:

curl -s -o /dev/null -w "%{http_code}" https://theirdomain.com/llms.txt

200: the platform publishes an llms.txt file, the emerging standard for telling AI systems what a site contains and where to find its structured content. Real infrastructure.
404: there is no llms.txt. Whatever "AI optimization" is being sold, there is no published, fetchable file behind it for an AI crawler to use.

A 404 is not automatically disqualifying, but it means the AI claim is not backed by the one thing AI systems actually consume. A 200 is the price of entry.

Run it on us: https://pressonify.ai/llms.txt returns 200.

The endpoint checklist

llms.txt is the start, not the finish. A platform serious about AI discovery exposes a small family of machine-readable surfaces. Fetch each one the same way:

/llms.txt and /llms-full.txt: the AI-readable site map and content index.
/ai-discovery.json: a structured manifest of what the site publishes for AI systems.
AI Discovery Protocol endpoints and Open Knowledge Format concept docs (for example /okf/index.md): structured, cross-linked versions of the actual content, in the markdown and schema formats AI systems parse most reliably.

Each of these should return 200 and readable content, not a redirect to a marketing page. On Pressonify, /ai-discovery.json and /okf/index.md both return 200 and serve real structured data. If a platform cannot point you to endpoints like these, its "AI infrastructure" lives in a slide deck, not on the web.

The higher bar: closed-loop verification

Publishing infrastructure gets you readable by AI. It does not prove an AI system ever cited you. That is a separate, harder claim, and it is where almost every platform stops.

Closed-loop verification means the platform does not just optimize for citations, it confirms them. After your release is published, it queries AI engines and checks whether they actually cite your content, then shows you the citation with the source and the date. This is the difference between "we optimize for AI visibility" and "here is the AI answer that cited you."

Most platforms cannot show you a single confirmed citation. Ask directly: after I publish, will you tell me when an AI engine cites this release, and show me the receipt? If the answer is a dashboard of impressions rather than a captured citation, you are being sold reach, not proof. We built Pressonify around the closed loop for exactly this reason, and we document real captured citations.

The "AI Optimization Score" red flag

A score is a useful summary only if there is fetchable proof underneath it. When a platform leads with a proprietary "AI Optimization Score" but cannot point you to a public llms.txt, discovery endpoints, or a captured citation, the score is a marketing number. It measures nothing an AI system can read.

In July 2026, a sponsored piece promoted one distribution service and its "AI Optimization Score" using GEO language, while its underlying mechanism was paid syndication to established outlets. Based on that piece, the service did not reference a public llms.txt, discovery endpoints, or citation-verification data. That is the pattern to watch for: AI vocabulary bolted onto a traditional wire, with no machine-readable proof. A score you cannot verify is not evidence. The endpoints you can fetch are.

Run the full audit in five steps

Fetch /llms.txt. Note the status code (200 or 404).
Fetch /ai-discovery.json and any advertised AI or discovery endpoints. Confirm they return real structured content, not a redirect.
Ask the platform to show you one confirmed AI citation, with the engine, the source, and the date.
Ask what happens after publication: does it verify citations, or only report distribution?
If a platform sells an "AI Optimization Score," ask which fetchable files and endpoints back it. Then fetch them.

Everything on this list is something you can check without trusting anyone's marketing, including ours.

Where Pressonify stands

We publish this test because we pass it. Pressonify's llms.txt, ai-discovery.json, and Open Knowledge Format endpoints are live and fetchable, our releases carry structured Schema.org markup and IndexNow indexing, and our citation tracking closes the loop by confirming when AI engines cite your work. Pricing is EUR 49.95 per release, EUR 9.95 for your first release.

If you are evaluating AI press release platforms, do not take the "AI-optimized" label on trust from anyone, us included. Fetch the files. The ones that are real will answer.