How Top YC Startups Get Discovered by AI Engines

We have audited the biggest YC success stories alongside their youngest batch mates. The score gap tells a story about what changes as startups mature - and what could have been done from day one.

Stripe: 82. Comprehensive help documentation structured as Q&A. Multiple schema types across every page type. Explicit AI crawler policies. An llms.txt-equivalent in their developer docs that gives AI a structured overview of their entire product surface.

Notion: 78. Extensive template gallery with structured metadata. Help center with hundreds of searchable Q&A entries. Strong entity authority through consistent branding across platforms.

Airbnb: 71. Rich structured data for every listing type. Help center with thousands of entries. BreadcrumbList schema creating navigable content hierarchies.

Average current-batch YC startup: 38. A landing page. Maybe a blog. No llms.txt. Default robots.txt. No FAQ.

The gap is 35-44 points. That is not a resource gap. Stripe did not need a 4,000-person engineering team to add Organization schema to their homepage. They needed one person who knew it mattered.

Strip away the brand differences and the top-scoring YC alumni share five infrastructure patterns:

1. Exhaustive help documentation Not 5 FAQ questions. Hundreds. Every product feature, every integration, every common error - documented as searchable Q&A. This creates massive citation surface area. When someone asks AI about any aspect of the product, there is a parseable answer waiting.

2. Schema depth beyond the basics Not just Organization. Article on every blog post. FAQPage on help pages. Product/Offer on pricing. BreadcrumbList for navigation. HowTo for tutorials. 4+ types minimum. Claude's compound trust multiplier rewards this stacking.

3. Developer documentation as AEO infrastructure API docs, SDK guides, integration tutorials - these are not just for developers. They are citation-ready content that AI extracts constantly. "How do I integrate Stripe with Next.js?" - the answer comes from Stripe's docs because they are structured as extractable Q&A.

4. Consistent entity signals everywhere Same company name in schema, footer, social profiles, Crunchbase, LinkedIn. No variations. No inconsistencies. AI cross-references these signals to verify entity authenticity.

5. Explicit AI welcome mat robots.txt with specific AI crawler rules. llms.txt or equivalent structured overview. Content licensing that explicitly permits citation. These are signals that say "yes, use our content in your answers."

None of these require enterprise-scale resources. All of them can be implemented by a two-person startup on launch week.

Here is the uncomfortable truth: 80% of what makes Stripe score 82 is infrastructure you can build in a week.

Week 1: Foundation - llms.txt with full product and company description - Organization + WebSite JSON-LD on every page - robots.txt with explicit AI crawler allows - 20-item FAQ with FAQPage schema

Week 2: Content depth - Product comparison pages answering "How does [you] compare to [competitor]?" - Integration guides structured as Q&A ("How do I connect [product] to Slack?") - Help center with 30+ entries covering setup, billing, and common issues

Week 3: Authority signals - Author bios on all content with Person schema - Consistent NAP (Name, Address, Phone) across site, LinkedIn, and directories - sameAs links to all social profiles and Crunchbase

Week 4: Coverage - Article schema on every blog post and guide - BreadcrumbList schema for site navigation - HowTo schema on tutorial content - sitemap.xml with accurate lastmod timestamps

Four weeks. No agency. No budget beyond your existing tools. You will not hit 82 - that takes years of content accumulation. But 60-65 is realistic, and that puts you in the top 5% of current-batch startups.

The top alumni have something you do not: years of accumulated content. Stripe's help center has thousands of entries. Notion's template gallery has tens of thousands of pages. You cannot replicate that volume in a month.

But volume is only part of the score. Structure is the other part. And structure does not require volume.

A 20-item FAQ with perfect FAQPage schema scores the same on that criterion as a 2,000-item help center with no schema. An llms.txt file on a brand new domain is just as readable to AI as one on a 10-year-old domain.

The criteria where alumni have an unbeatable advantage: Content Velocity (years of publishing), Entity Authority (brand recognition), Original Data (proprietary datasets). These take time.

The criteria where you can match them immediately: llms.txt, Schema.org, robots.txt, Clean HTML, Semantic HTML, FAQ content, Q&A format, Internal Linking. These are technical, not temporal.

Focus on the criteria you can win today. The rest compounds as you grow. The point is not to match Stripe tomorrow - it is to start the compound curve that gets you there.