Growth idea action plan
Publish llms.txt for agent retrieval
Add a root /llms.txt file that tells AI agents what the site is, which URLs matter, and how to interpret the content.
Why this can grow a startup
AI agents often need compact context before deciding which pages to fetch. A concise Markdown index reduces ambiguity, points models to authoritative URLs, and makes the catalogue easier to cite or summarize without forcing the model to infer the whole product from JavaScript-rendered pages.
Ian's take
From scaling consumer platforms across MENA and Southeast Asia, my default is to distrust growth work that only looks good in a slide. For SEO and AI search, I care less about clever keyword tricks and more about clarity. A buyer, crawler, or answer engine should quickly understand who this is for, why it works, what proof backs it, and what page deserves to be cited. I would run it small enough to learn quickly, then only scale the parts that real users repeat, save, reply to, or buy from. For this tactic, I would watch one clear growth signal before putting more time or budget behind it.
Action plan
- Define one narrow startup segment where publish llms.txt for agent retrieval can create a measurable lift.
- Turn the tactic into one offer, page, campaign, or workflow for the SEO and AI Search channel.
- Use the evidence from llmstxt.org to set the first version of the message, format, and audience.
- Launch a small test for 7 to 14 days with one success metric: one measurable growth signal.
- Review the result, keep the winning message, remove weak variants, and turn the learning into a repeatable growth playbook.
Source-backed example
The llms.txt proposal standardizes a root Markdown file with a short project summary and curated file lists for model and agent consumption.
Source: llmstxt.org
Last checked: May 19, 2026
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