This automation helps investors screen a high volume of inbound deals by scoring each one against a consistent rubric covering team, market, traction, and fit. It surfaces the deals worth a partner's time.
What's Included
- Thesis rubric: Score deals on team, market size, traction, and thesis fit.
- AI assessment: Agents draft a first-pass score from the deck and notes.
- Ranked queue: Sort screened deals so the strongest rise to the top.
- Pass rationale: Generate a polite, consistent pass note for deals below the bar.
How To Use
- Define your screening rubric and weights in Taskade.
- Feed new deals from your pipeline into the scorecard.
- Let an AI agent draft scores and a recommendation.
- Review the ranked queue and advance or pass on each deal.
Use AI agents to assess deals and find more investor automations for deal evaluation.