Find 10 investor matches + direct emails from your pitch — stop manual scraping

Founders and early-stage teams—roughly part of the 2.0M startups formed globally each year—waste dozens of hours manually scraping databases, hunting for investor theses, and guessing at contact emails, which slows fundraising and disproportionately hurts solo founders and non-networked teams. The pain is acute because a few quality intros can make or break a round, yet existing tools return noisy matches or locked contact information, so founders either pay expensive broker fees or accept low-probability cold outreach. You could build a B2B SaaS that lets a founder upload a one-page pitch or deck and returns 10 prioritized investor matches with verified direct emails, a match-score explanation powered by LLMs and embeddings, and ready-to-send outreach templates synced to CRMs; pricing could target the $1,500 ACV buyer segment implied in the $3.0B market estimate. The product would combine automated semantic matching, active email verification and deliverability checks, and a feedback loop that refines models from accepted intros and investor responses. This is an attractive moment: market score 94/100 and revenue potential 86/100 reflect strong demand, and trends—AI-driven semantic matching, growing acceptance of cold digital intros by VCs, and founder willingness to pay for curated introductions—lower adoption friction. To stand out in a medium-competition landscape you must deliver measurably higher precision (fewer false positives), transparent match rationale, and superior contact hygiene, while acknowledging hard challenges such as maintaining fresh contact data, managing deliverability and compliance, and proving unit economics; a focused pilot with clear KPIs will be essential to decide whether to scale.