Generate a pitch deck that explains your AI moat, data advantage, and cost structure the way AI-native investors expect.
AI-specialist funds — Greylock, Sequoia AI, Bain AI, Felicis — are saturated with "we do X but with AI" decks. This template prompts you to answer the three differentiators they actually care about: what's your data moat, what's your model advantage (or orchestration), and how do unit economics work when inference cost moves.
10 slides tuned for AI startups. DamnSlides fills each with content specific to your company and topic.
Product name, core AI capability, and target user persona.
Specific cognitive or manual workflow broken by current tools.
Solution framed in terms of what the AI can do that a human can't, faster.
Market in terms of labor hours replaced or workflows compressed.
Product flow showing input → model → output with trust signals.
Early design partner logos, accuracy or productivity gains measured.
Revenue model: seat, usage, or outcome-based with margin assumptions.
Moat analysis: data flywheel, distribution, or domain-specific RL.
Founders from relevant labs (OpenAI, DeepMind, Anthropic, Meta AI).
Round to fund compute, data acquisition, and first commercial motion.
Enter your AI context — company, product, market, specifics.
DamnSlides plans a pitch deck structured for AI audiences.
Click any slide to edit, regenerate, or rewrite. Export to PPTX.
No. Most successful AI startups today use foundation models from OpenAI, Anthropic, or open-source. Your moat is data, workflow, and distribution — not model ownership. Explain your orchestration and fine-tuning strategy.
Head-on, on a slide. Show the specific vertical knowledge, integrations, or data you accumulate that a horizontal platform won't replicate. Investors will ask, so answer preemptively.
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