Help addiction researchers find collaborators via AI-driven matching & data sharing

Addiction researchers across roughly 20,000 global institutions—academic centers, public-health units, clinical networks, and small labs—struggle to find compatible collaborators and share interoperable datasets, relying on informal networks, manual CV review, and keyword searches that miss interdisciplinary fits and slow grant timelines. That friction is increasingly costly as funders push for multi-site studies and FAIR data, and it disproportionately penalizes smaller teams and community partners who lack visibility and legal/data-sharing infrastructure. The result is delayed proposals, suboptimal study designs, and missed opportunities for larger, more reproducible research. You could build an AI-driven, addiction-focused platform that combines semantic author and topic extraction, automated collaborator and grant matching, FAIR-compliant data-sharing pipelines, templated IRB/DSA workflows, and integrations with common repositories and institutional systems. This vertical approach leverages maturing AI discovery tools and rising open-science mandates; the addressable market is roughly $1.20B (20,000 institutions x $60,000 ACV), with a market score of 92/100 and revenue potential of 82/100, suggesting commercial viability if adoption accelerates. A niche, domain-specific network can stand out by offering curated moderation, domain taxonomies for addiction research, enterprise security and compliance for cross-border data flows, and metrics that demonstrate faster time-to-collaboration and improved grant competitiveness. Honest challenges remain: competition is medium, network effects require early anchor institutions, data quality and IRB/privacy constraints are non-trivial, and proving measurable ROI to institutions will be essential before scaling.