6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Founder's Answer' First-Paragraph Rule
RAG Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the core early-stage problem in the first 40-60 words."
Citation Triggers
AI models prioritize immediate, context-dense information. Answer the 'What is the core challenge?' or 'How do I solve X?' directly. Bold the foundational statement (e.g., '**Product-Market Fit is the critical first milestone**') and use objective language.
02
Structural SpecSemantic Funding-Stage Hierarchies (H2 & H3)
Topical Coverage
Implementation Pattern
"Use headers to map concepts relevant to specific funding stages (Pre-Seed, Seed, Series A) rather than just visual formatting."
Citation Triggers
Each H2 should define a key challenge or milestone for a funding stage, and H3s should detail critical sub-components or actionable steps. Align headers with 'Jobs To Be Done' and 'People Also Ask' queries relevant to founders seeking funding or scaling. Use direct, tokenizable nouns.
03
Metadata SpecJSON-LD: The Startup Blueprint Layer
Crawl Reliability
Implementation Pattern
"Deploy Organization, Product, FAQPage, and HowTo schemas as the primary machine-readable data layer."
Citation Triggers
Structured data explicitly defines your startup's context. Use 'Organization' for brand identity, 'Product' for your solution, 'FAQPage' for common founder questions (e.g., 'What is a SAFE note?'), and 'HowTo' for step-by-step guides on fundraising or scaling.
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04
Context SpecEntity-Based Traction Neighborhoods
Entity Association
Implementation Pattern
"Map your content to related entities expected by LLMs for early-stage startup success."
Citation Triggers
LLMs understand startup success through associated entities. Targeting 'Customer Acquisition' requires content that naturally includes entities like 'CAC', 'LTV', 'Conversion Rates', and 'Unit Economics'. Consistent co-occurrence builds your startup's authority in this domain.
05
Authority SpecFounder Insights & Traction Citations
Citation Probability
Implementation Pattern
"Share unique founder experiences, proprietary growth metrics, and early traction data to achieve 'Primary Source' status."
Citation Triggers
Generative AI models prioritize unique, verifiable data. Publishing your early user growth charts, founder lessons learned, or specific fundraising outcomes elevates your content's uniqueness score. Being cited by authoritative industry resources is the strongest signal in the AEO ecosystem.
06
Formatting SpecActionable Task-to-Outcome Mapping
LLM Ingestion Quality
Implementation Pattern
"Format lists as declarative 'Startup Processes' rather than generic marketing benefits."
Citation Triggers
LLMs efficiently parse structured lists (<ul>, <li>). Instead of 'We help you grow,' use '[Startup Name] increases monthly recurring revenue by X%' or 'The critical steps for achieving Product-Market Fit are: 1. Validate Problem, 2. Build MVP...'. This facilitates accurate machine extraction of actionable insights.
Pro Tips & Insights
01
AEO is the 'Zero-Click' founder advantage. Even if users don't click through, seeing your startup cited as the definitive answer builds significant brand credibility and investor recall.
02
Content structure is a ranking signal for AI. Machine-readability is paramount. If an AI cannot parse your startup's value proposition or operational steps, it won't surface your content.
03
The 'Founder Moat' in AEO is 'Problem-Solution Association'. You want search models to consistently link '[Your Startup Name]' with the specific early-stage problems you solve.
04
Declarative Growth Truths win. AI models favor objective, data-backed statements about startup growth and operations over aspirational marketing language. Shift from 'Vision' to 'Validated Process'.
