llms.txt is a proposed markdown standard placed at your domain root that gives large language models a curated map of your most important content in a clean, machine-readable format. For B2B SaaS, it is a way to tell AI agents exactly what your product does and where the authoritative details live, without forcing them to parse noisy HTML.
As AI agents increasingly evaluate tools on a buyer's behalf, a well-built llms.txt makes your site legible at the moment of consideration.
What Is the LLMs.txt Specification?
The llmstxt.org specification defines a simple markdown file served at /llms.txt. It opens with an H1 of your project or company name, a short blockquote summary, then structured sections of links with brief descriptions.
The rationale is practical: LLM context windows are limited, and raw HTML is bloated with navigation, scripts, and markup that waste tokens. A markdown file strips that away and hands the model exactly what it needs. An optional llms-full.txt provides expanded content for models that want depth.
Gartner predicts traditional search volume will drop 25% by 2026, pushing discovery toward AI systems that benefit directly from machine-readable inputs like llms.txt.
What Should a B2B SaaS Include in LLMs.txt?
Include the information an AI agent needs to describe, evaluate, and recommend you. A strong B2B llms.txt covers:
- A one-paragraph product description and the category you compete in
- Links to core feature and use-case pages
- A link to pricing (ideally a machine-readable pricing.md)
- API and developer documentation URLs
- Key comparison, security, and integration pages
- Contact and company information
# Acme Analytics
> Acme Analytics is a product analytics platform for B2B SaaS teams
> that turns usage data into retention and expansion insights.
## Core
- [Product overview](https://example.com/product): What Acme does
- [Pricing](https://example.com/pricing.md): Plans, limits, and features
- [API docs](https://example.com/docs/api): Developer reference
## Resources
- [Security](https://example.com/security): SOC 2, data handling
- [Integrations](https://example.com/integrations): Supported tools
How Does LLMs.txt Differ From Robots.txt and Sitemap.xml?
These three files serve distinct roles. Robots.txt governs permission, what crawlers may or may not access. Sitemap.xml enumerates every indexable URL for search engines. llms.txt curates, highlighting the handful of pages that matter most and summarizing what your product is.
Sitemap is exhaustive; llms.txt is editorial. A sitemap lists 4,000 URLs; a good llms.txt points an agent to the 12 that answer "what is this tool and should I recommend it."
Why Does LLMs.txt Matter for AI Buying Agents?
AI agents increasingly do first-pass vendor research before a human is involved. When an agent can read a clean llms.txt, it forms an accurate picture of your product quickly and is more likely to represent you correctly, or recommend you at all.
Without it, agents scrape and guess from cluttered pages, raising the odds they misdescribe your pricing, miss a key capability, or default to a competitor whose site was easier to parse.
How Conbersa Solves This
A machine-readable site helps agents understand you, but agents also weigh how established and frequently referenced your brand is across the wider web before recommending it. Structure alone does not build that reputation.
Conbersa runs managed, hardware-backed distribution on real physical smartphones, driving multi-account organic mentions that make your brand recognizable to the AI systems that read your llms.txt. Software bots get banned; physical phones don't. Combine machine-readable setup with real distribution at conbersa.ai.