robots.txt AI Policy Design
Your robots.txt is now a policy document. It decides whether AI models may train on your content, whether AI search engines may cite you, and whether AI assistants may fetch your pages when a user asks. Most sites answer these three questions by accident. We answer them on purpose — in writing, with you.
The decision nobody made consciously
Three different things now knock on your server door wearing "AI" name tags. A training bot wants your content to teach a model — value returned: indirect at best. A search bot wants to index you for AI search results — value returned: citations and traffic. A user agent fetches your page live because a human asked about you — value returned: that human, reading your page. One robots.txt line treats all three the same. That's rarely what a business actually wants.
What you get
- A policy conversation first: plain questions — do you want to be in AI answers? do you care if models train on your blog? — before a single line is written.
- The tiered file: explicit rules per crawler, current tokens verified against each operator's live documentation (they change more often than anyone admits).
- Content-Signal and tdm-reservation headers set to match the policy, so your intent is machine-readable at every layer.
- The silent-failure sweep: we check your CDN and security layers aren't quietly overriding the policy you just paid to design.
- A one-page policy memo in plain English — what you allow, what you block, why — so the decision survives staff changes and future audits.
Our own file is the demo
SynapseIN's robots.txt is public, deliberately structured, and explicitly welcomes 24 AI crawlers — because our policy decision is maximum AI visibility. Yours might be different. The point is that it should be a decision.