Currently, the process of matching journalists with expert sources on platforms like HARO suffers from inefficiencies: journalists manually sort through irrelevant pitches, experts waste time crafting responses with low success rates, and potentially great matches get lost in the noise. This reduces the value for all parties involved.

A smarter way to connect sources and storytellers

The core idea involves enhancing such platforms with AI to create a more efficient two-sided marketplace. Natural language processing could analyze journalist queries and expert profiles to suggest the best matches automatically. For experts, AI could draft initial pitch responses based on their past work or profiles. Journalists would receive pre-filtered, relevant suggestions rather than sifting through all submissions.

Key features might include:

• Smart matching algorithms that understand both query context and expert qualifications
• Automated pitch drafting for experts who opt in
• Quality filters to flag off-topic or low-effort submissions

Expanding beyond traditional journalism

This approach could extend to connecting podcasters with guests, researchers with interview subjects, or any scenario requiring expert sourcing. The same matching technology could adapt to different content formats by learning from successful pairings in each domain.

Existing platforms show the model works in manual form - the opportunity lies in dramatically improving efficiency through automation while maintaining human editorial judgment where it matters most.