Systematic Discovery for Product-Market Fit

My guest today is Jesse Zhang. Jesse is the co-founder and CEO of Decagon, one of the fastest-growing AI customer service companies. Decagon provides a centralized AI engine to auto-resolve issues at any time, in every language, and across every channel. Jesse shares his systematic approach to finding product-market fit by asking potential customers exactly how much they'd pay for solutions. We explore why customer service and coding have emerged as the two clearest AI use cases for enterprises, and the key business and technical factors behind Decagon's momentum. We discuss the intense competitive dynamics of building in AI today, strategic decisions around building proprietary models, and deploying AI agents at enterprise scale. Please enjoy my conversation with Jesse Zhang.

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Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠).

Show Notes:

(00:00:00) Welcome to Invest Like the Best

(00:05:49) Building a Company in a Competitive Market

(00:07:26) Personal Background and Competitive Upbringing

(00:10:32) Challenges and Lessons from Previous Ventures

(00:12:21) Ideation and Customer Discovery Process

(00:19:31) Developing and Refining AI Customer Service Agents

(00:32:26) Voice AI and Future Prospects

(00:38:20) Utilizing Customer Interaction Data

(00:39:59) Frameworks for Implementing AI in Business

(00:41:48) Evaluating the ROI of Coding Agents

(00:42:53) The Future of Company Agents

(00:45:15) Brand Personality in AI Agents

(00:47:48) Investor Interest in AI Companies

(00:54:32) The Competitive Landscape of AI Talent

(00:57:21) Building Proprietary AI Models

(01:10:36) Customer Qualification and Engagement

(01:17:27) The Kindest Thing