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These modular AI prompts are designed for Deep Tech and Vertical AI diligence — covering market sizing, competitive analysis, technical moat assessment, and founder evaluation. Use them with any LLM.
| Category | Prompt Focus | When to Use |
|---|---|---|
| Market Sizing | TAM/SAM/SOM with bottom-up validation | Before first meeting |
| Competitive Moat | Technical differentiation vs. alternatives | After product demo |
| Founder Analysis | Domain expertise and founder-market fit | During reference checks |
| Unit Economics | LTV/CAC, payback period, gross margin | After receiving model |
| AI Risk Assessment | Disruption risk from foundation models | For AI-adjacent companies |
| Regulatory Mapping | Compliance risk and certification needs | For regulated industries |
| Reference Generation | Customer and expert reference questions | Before reference calls |
AI enables rapid market research that would take analysts days to compile. Use prompts to generate initial competitive landscapes, identify key players, and map the regulatory environment. Then validate with primary research. AI handles breadth; experts handle depth.
For Deep Tech and Vertical AI companies, assessing technical moat requires understanding of ML architecture, data advantages, and compute economics. Structured AI prompts can generate a technical diligence framework that helps non-technical investors ask the right questions.
AI-generated reference questions tailored to the company’s claimed differentiation extract more useful signal than generic reference calls. If a company claims ‘best-in-class NLP,’ your reference questions should test whether customers actually chose them for NLP accuracy vs. price, support, or integration.
The most powerful use of AI in diligence is pattern matching across a portfolio of opportunities. Consistent prompting across all deals in a category reveals differentiation (and lack thereof) more clearly than evaluating each deal in isolation.
Leading VC firms are using AI for: market sizing and competitive landscape generation; financial model analysis and red flag identification; reference call question preparation; patent and technical literature review for deep tech; and synthesizing large data rooms into investment memos. AI speeds up the breadth work, freeing analysts and partners to focus on judgment-intensive primary research.
The most valuable VC diligence prompts: (1) ‘List the top 10 competitors to [company] in [space], with estimated revenue and differentiation’; (2) ‘What are the 5 biggest risks to [business model] from AI disruption?’; (3) ‘Given these unit economics [paste data], what are the key questions about CAC payback?’; (4) ‘Generate 10 reference questions to validate [specific claim] for a [sector] company.’ Modular, specific prompts beat generic ones.
AI is replacing the lower-value research tasks that occupied analyst time — market scans, competitive mapping, data room digestion, memo first drafts. It’s not replacing the judgment-intensive work: founder assessment, market call, board dynamics, and portfolio support. The analyst role is evolving toward higher-order synthesis and primary research, with AI handling the breadth layer. Firms that adapt their analyst roles to this shift will have a significant efficiency advantage.