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Small VC firms (3-10 people) managing AI-focused funds ($7.5M-$60M) receive 50-100 AI startup pitch decks per month, but manually classifying and prioritizing them takes a single analyst 10-15 hours per week. Generic CRM tools lack the ability to filter by AI tech stack criteria (model type, training data, GPU usage, etc.).
Automatically parses IR emails and pitch deck PDFs, converting them into structured deal cards with AI tech stack, team composition, and traction metrics. Includes automatic AI domain tagging (NLP, CV, robotics, etc.), auto-scoring based on fund investment criteria, and analyst comments and status management — an AI-specialized dealflow CRM.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |