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SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| ChatGPT | Not provided | AI Assistant | Users search product questions on it |
| Perplexity | Not provided | AI Search Engine | Users search product questions on it |
| Cursor | Not provided | AI Code Assistant | Generates working code from documentation |
| Apidog | Not provided | API Development | Used to design, test, and document APIs in one place |
| Context7 | Not provided | AI Tool | Feeds current information to AI agents (mentioned in comments) |
| llms.txt | Not provided | AI Documentation | File at web root for AI crawlers (like a sitemap) |
USER NEEDS
Pain Points:
- Users getting wrong answers from AI tools due to poor documentation
- Documentation is unclear, outdated, or poorly structured
- AI spreads confusion at scale when documentation is subpar
- Legacy docs are like random notes and need optimization
- Lack of AI-friendly structure increases support burden
Problems to Solve:
- Ensure AI tools provide accurate answers about the product
- Reduce repetitive support questions
- Speed up developer onboarding
- Avoid hiring more support staff as usage grows
- Make documentation readable for both humans and AI
Potential Solutions:
- Rebuild documentation to be AI-friendly with clear structure
- Keep documentation synced with actual API specs
- Use tools like Apidog for integrated API design/testing/documentation
- Implement llms.txt file for AI crawlers
- Use AI tools (like Cursor) to test documentation quality
- Provide proper context in documentation for better AI understanding
- Fix inconsistencies flagged by tools like AICarma (mentioned in comments)
GROWTH FACTORS
Effective Strategies:
- Optimizing documentation for AI consumption (Generative Engine Optimization)
- Reducing support burden through better self-service resources
- Improving developer onboarding speed
Marketing & Acquisition:
- Not explicitly mentioned in the content
Monetization & Product:
- Aligning product documentation with actual API specs to ensure accuracy
- Feature development focused on AI compatibility (e.g., MCP server for code generation)
- Product-market fit: Adapting to user shift toward AI-based information retrieval
User Engagement:
- Using documentation as frontline user support
- Community building through shared best practices (e.g., llms.txt implementation)
- Proactively fixing inconsistencies based on AI trend analysis (e.g., AICarma)