Finding the right people to talk to — discover, qualify, and reach them

Many teams—product researchers, recruiters, growth marketers, and independent consultants—struggle to reliably find and engage the right people to interview or sell to, spending weeks on manual discovery, enrichment, and low-response outreach. The problem scales: roughly 500 million knowledge workers represent a $50.0B addressable market if each spends about $100/year on sourcing, research, and outreach tools, yet current workflows leak time and budget into false positives and low-quality contacts. You could build an ML-driven platform that aggregates permissioned public signals (public posts, open-source commits, product launches), scores intent, enriches profiles, and automates compliant outreach with integrated incentives and panel management. Offer modular revenue: subscriptions for teams, per-recruit fees for researchers, and enterprise integrations/APIs for research-as-a-service partners. The MVP goal would be measurable: reduce time-to-qualified-contact from weeks to days and increase meaningful responses while providing audit trails for consent and quality control. Market timing is favorable—our Market Score of 92/100 and Revenue Potential 84/100 reflect three concurrent trends (a growing side-hustle economy, better platform signal availability for ML, and normalization of research-as-a-service), and competition today is medium. To stand out you must combine higher-precision intent signals with strict privacy/compliance, transparent quality guarantees, workflow integrations, and channel partnerships; be honest that regulatory risk, data access limits, and the operational cost of maintaining high-quality panels are real challenges, so prioritize consented sources and defensible ML features before scaling outreach.