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 reader, crawler, or AI search tool 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
GrowthDex source hub: llmstxt.org
Last checked: May 19, 2026
Adjacent tactics in the same lane
If this page is close to your problem, these tactic pages usually belong in the same working set.
- Answer-first source citation pages 3 shared channels · 2 shared stages
- Source-dated technique dataset 3 shared channels · 2 shared stages
- Startup-learning post backlink wedge 2 shared channels · 2 shared stages
- Idea-seeding content before launch 2 shared channels · 2 shared stages
Related GrowthDex essays
- The machine reader is part of the audience now SEO, AI discovery, content systems
Read GrowthDex essays
The Blog turns real growth tactics into plain-English case studies by niche, channel, and buying situation.
Why this is worth your time
GrowthDex starts with tactics that founders, marketers, and product teams have actually tried. Each essay turns the evidence into a practical move you can test without pretending one case study is a guarantee.
Ian Goh has helped grow consumer platforms across Southeast Asia, India, and MENA. His work includes scaling Tiki to 100M+ users, doubling BIGO's MENA revenue in 7 months, and increasing OYO's direct booking share across 6 Southeast Asian markets.
- Helped scale Tiki to 100M+ users.
- Doubled BIGO's MENA revenue in 7 months.
- Raised OYO's direct booking share by 50% across 6 Southeast Asian markets.
Want help turning this into a growth system?
If you want someone to pressure-test this against your real market, Ian works with founders on growth, market entry, and operator-led distribution.
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