For twenty years, being findable online meant one thing: ranking on Google. That still matters, but it is no longer the whole story. A growing share of commercial real estate research now starts as a question to an AI assistant — what office space is available near the convention center, which buildings downtown have covered parking — and the answer comes back as a synthesized recommendation, not a page of blue links.
If AI assistants are going to answer questions about your market, you want them answering with accurate information about your building. That is the problem llms.txt exists to address, and it is why every Floorplate site ships with one. Here is what the file is, why it matters for commercial real estate specifically, and what Floorplate handles on your behalf.
What llms.txt actually is
llms.txt is a plain-text file that lives at the root of a website, alongside familiar files like robots.txt and sitemap.xml. Where robots.txt tells search crawlers what they may access, llms.txt is written for large language models: a clean, structured summary of what the site is about and where its important content lives, in a format AI systems can read without fighting through navigation menus, scripts, and layout code.
It is an emerging convention rather than a settled standard — proposed in the AI community as a practical way for websites to introduce themselves to language models. Adoption keeps growing for a simple reason: it is cheap to provide and genuinely useful to machines. A single readable file beats forcing a model to reverse-engineer a website's structure and hoping it guesses right.
Why AI discovery matters for CRE
Commercial real estate is exactly the kind of research AI assistants are good at: high-stakes, detail-heavy, and scattered across many sources. A tenant rep asks an assistant to shortlist buildings with a certain footprint in a certain submarket. An out-of-town prospect asks what surrounds a property before booking a visit. In every case, the assistant assembles its answer from the sources it can actually read and reasonably trust.
Buildings with a clear, machine-readable presence are simply easier for these systems to represent correctly. Buildings without one get described secondhand — from aggregator scraps, expired listings, or nothing at all. Nobody can guarantee placement in an AI-generated answer, any more than anyone can guarantee a Google ranking. What you can control is whether the accurate version of your property exists, and whether it is cheap for a machine to consume.
What Floorplate does for you
Floorplate provides an llms.txt file for every property site it manages, describing your site's content in the structured form AI systems prefer. It sits alongside the fundamentals that serve both AI and traditional search: clean block-based pages for availabilities, amenities, location, and contact, plus a professionally managed domain. There is nothing for you to configure and nothing to maintain — it is part of the service, kept in step with your site as it changes.
The broader point stands regardless of any single file format. Discovery is fragmenting across search engines and AI assistants, and the properties that stay easy to find will be the ones whose information is accurate, structured, and effortless for machines to read. That is a maintenance discipline, not a one-time trick — which is exactly the kind of work a managed service exists to absorb.