Technical Positioning Prep
Before your first investor meeting, prepare:
- Model architecture summary (1 page, non-technical language for investors) - Defensibility thesis: What do you have that competitors can't easily replicate? Proprietary data, specialized architecture, domain expertise, regulatory advantage - Benchmark data: How does your model perform relative to alternatives? Be specific and honest — overstating performance destroys credibility - Technical roadmap: What improvements are planned and what resources they require - Team credentials: AI investors evaluate technical talent heavily — highlight publications, prior companies, relevant expertise
Financial Model Requirements
AI-specific financial modeling that investors expect:
- Compute cost projections: Training costs, fine-tuning costs, inference costs at scale. Include GPU/TPU pricing assumptions and efficiency improvements over time - Unit economics: Revenue per customer minus compute, support, and infrastructure costs. Show that unit economics improve with scale - R&D vs. revenue timeline: When does the company transition from R&D-heavy to revenue-generating? Be realistic — investors have seen too many AI companies promise revenue in 6 months and deliver it in 24 - Hiring plan: AI talent is expensive. Model realistic compensation for ML engineers, researchers, and data scientists - Infrastructure spend: Cloud costs, data storage, tooling subscriptions
Investor Targeting for AI
Build your target list with AI-specific filters:
- AI-specialized funds: Radical Ventures, AIX Ventures, Conviction Partners - Generalist VCs with AI thesis: a16z, Sequoia, Greylock — but target the partner who covers AI specifically - Corporate VCs with AI interest: Google Ventures, Microsoft's M12, NVIDIA's NVentures, Salesforce Ventures - Check portfolio for conflicts: Does the fund already have a company doing something similar? If yes, remove from list - Vertical fit: Application-layer AI companies should target SaaS investors who understand AI. Infrastructure companies should target deep tech investors
Demo and Materials Prep
AI investors expect:
- Live demo: Show the product working with real or realistic data. Don't use cherry-picked examples — sophisticated investors will ask to try their own inputs - Technical appendix: Detailed architecture, training methodology, and evaluation metrics for investors who want to go deep (usually 1-2 per fund) - Data strategy document: Where does your training data come from? Is it licensed, proprietary, public? What are the risks? - Competitive landscape: Position against both AI competitors and non-AI alternatives. The question isn't just 'are we better than other AI companies' — it's 'are we better than the existing solution, which may not use AI at all?' - IP documentation: Patents filed, trade secrets, licensing agreements for foundational technology
Ready to Position Before You Pitch?
The Strategic Capital Review is a 30-minute call to assess your raise readiness and determine whether access to our investor network is relevant to your situation.
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