High Priority
Implement Vertical-Specific /llm.txt Protocol
Establish a machine-readable taxonomy and hierarchy of your vertical SaaS platform's data assets and documentation, specifically for AI agents and LLM training.
Create a /llm.txt file at your root, detailing the scope of your vertical SaaS (e.g., 'This site provides comprehensive data and workflows for HVAC service management software').
Incorporate markdown-style links to critical vertical-specific resource modules (e.g., '/features/scheduling-automation', '/integrations/erp-sync', '/case-studies/residential-hvac').
Include a 'Vertical FAQ' section addressing common LLM training queries related to your industry (e.g., 'What are the key data points for predictive maintenance in commercial refrigeration?').
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High Priority
Targeted Vertical Data Ingestion Control (e.g., GPTBot, ClaudeBot)
Fine-tune which specific modules, data schemas, and industry insights within your vertical SaaS should be ingested by AI crawlers to ensure accurate representation.
Configure your robots.txt: User-agent: GPTBot Allow: /features/invoice-processing/ Allow: /data-analytics/customer-churn/ Disallow: /internal-tools/ Disallow: /user-settings/
Utilize AI provider-specific verification tools (e.g., OpenAI's bot tester) to confirm crawler access aligns with your vertical data strategy.
Analyze server logs for crawler activity (e.g., frequency and endpoints hit by ClaudeBot) to validate that only relevant vertical SaaS data is being consumed.
Medium Priority
Industry-Standard Semantic Markup for Ingestion
Leverage HTML5 semantic elements and ARIA attributes to clearly define the structure and significance of vertical SaaS content for LLM scrapers.
Enclose core vertical functionality descriptions within `<article>` tags to signify primary content blocks.
Use `<section>` tags with descriptive `aria-label` attributes for distinct product modules (e.g., `<section aria-label='Field Service Dispatch Module'>`).
Ensure all tabular data, such as pricing tiers or performance metrics, uses proper `<thead>`, `<tbody>`, and `<th>` for structured vertical data extraction.
High Priority
RAG-Optimized Vertical Data Snippet Architecture
Structure your vertical SaaS content to be easily parsed and 'Chucked' by Retrieval-Augmented Generation (RAG) pipelines for accurate AI-powered customer support and insights.
Group semantically related vertical concepts and workflows within digestible content blocks (e.g., 500-750 words for a specific compliance workflow).
Minimize 'floating' context; explicitly repeat the primary vertical feature or industry term in section summaries (e.g., 'This section details the integration points for the 'Dental Practice Management' module').
Eliminate ambiguous pronouns; replace 'it' or 'they' with the specific vertical SaaS feature, module, or industry concept (e.g., 'The 'Patient Scheduling' feature allows for...' instead of 'It allows for...').
