LLM.txt & AI Crawler Setup Guide for Early-stage startups

Create a text file at /.well-known/founder.txt with a concise, high-level overview of your startup's mission and problem solved.

Include markdown-style links to your core landing pages, MVP feature descriptions, and investor deck summary.

Add a 'Problem/Solution' section to directly address common startup validation queries for AI research bots.

User-agent: InvestorBot Allow: /features/ Allow: /use-cases/ Disallow: /team-internal/

Verify your crawler permissions using a user-agent simulator to mimic potential investor intelligence bots.

Monitor crawl frequency in server logs, focusing on bots accessing your pricing, traction, and product pages to gauge interest.

Wrap key feature descriptions within `<section>` tags, using `aria-label` attributes to specify the feature name (e.g., `aria-label='Automated Onboarding'`).

Employ `<aside>` tags for testimonials or social proof related to specific features to isolate valuable endorsements.

Ensure all data points related to traction (e.g., user growth, MRR) are within `<table>` elements with `<th>` for clear labeling.

Isolate key traction metrics (e.g., '1000+ Beta Users', '$50k MRR') into distinct paragraphs or list items, ideally under 250 words.

Reiterate the core problem your startup solves at the beginning of each feature or benefit section to provide immediate context.

Replace vague references like 'our solution' with specific product names or feature functionalities (e.g., 'The AI-powered analytics dashboard').