Your own llms.txt as a structured entry point

When an AI assistant reads your site, it does not browse fifty pages the way a person would. It fetches what looks like a map, then follows the links that seem relevant. If that map is missing or unstructured, the assistant fills in the gaps with inference, and inference is where facts go wrong.

Spectare publishes an llms.txt on your domain generated from your live content library. It is a curated entry point that tells an agent what you publish and where to find each piece as clean JSON. Instead of scraping rendered HTML across dozens of URLs, an agent can read the index and fetch only what it needs, getting your actual words rather than a reconstruction of them.

The file updates automatically whenever you publish or edit an atom. There is nothing to maintain. The same library that personalises pages for human visitors is the source of record for machine readers, so the two are never out of sync.

As metered crawling becomes more common, a compact and accurate entry point is also a cost control for the agents reading you. A handful of fetches instead of a full crawl is a smaller bill for whoever is running the agent, and a smaller bill makes your content more likely to be read rather than skipped.