Video by Rasa via YouTube
In this episode of The Dialogue Architects, host Lauren Goerz sits down with Dino Letic, Product Manager at JetBrains, to explore what it takes to build conversational AI for developers.
Unlike most customer support audiences, developers arrive with detailed logs, highly technical questions, and unique environments that make support particularly challenging. Dino shares how his background in support engineering shaped his approach to building JetBrains’ unified support bot—an AI-powered experience designed to simplify customer interactions across a growing portfolio of products.
The conversation explores the challenges of fragmented support systems, the importance of creating a single point of entry for users, and how the bot identifies intent, asks follow-up questions, summarizes issues, and escalates conversations when needed. Dino explains how JetBrains balances automation with human expertise, why knowledge management is critical to AI success, and how the team handles the ongoing challenge of outdated documentation and rapidly evolving products.
Lauren and Dino also discuss measuring success, team ownership models, deployment considerations, and why JetBrains chose an on-premises AI approach. The episode concludes with a thoughtful discussion about the opportunities—and risks—of agentic AI in customer support and product decision-making.
Whether you’re building AI assistants, managing support operations, or designing enterprise conversational experiences, this episode offers practical lessons from one of the most technically demanding support environments in software.
Here’s What You’ll Hear
Why developers are uniquely challenging support customers
How JetBrains unified support across multiple products and teams
The role of AI in ticket triage, escalation, and issue summarization
Why a single conversational entry point improves customer experience
How JetBrains manages knowledge sources and documentation quality
The challenges of outdated information in AI-powered support
What success metrics matter most for support automation
Why JetBrains chose an on-prem deployment strategy
Team structures, ownership models, and operational lessons
Where agentic AI fits—and where caution is required
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