Kyle Nesbit, a longtime Googler and AI expert, joins us on Deployed to share lessons from his 17+ years at the forefront of distributed systems, machine learning, and AI-driven product innovation. 

Kyle has helped build foundational technologies like BigQuery and worked on early large language model (LLM) development at Google, giving him a unique perspective on how teams can successfully transition from traditional engineering to modern AI-focused workflows.

In this episode, we explore:

• The challenges and opportunities of transitioning traditional engineering teams to LLM development
• Why starting with evaluation metrics is the foundation for success
• Practical strategies for iterative improvement and guardrail design
• How to balance product priorities and quality trade-offs when scaling AI systems
• The real story behind AI demos and how to communicate progress effectively
• Foundational issues in data discovery and access—and why solving them matters more than chasing trends

In this episode of Deployed, we sit down with Sara Beykpour, Co-founder and CEO of Particle, to discuss how they're using AI to transform how people consume news. Particle, which just launched last week, organizes news coverage across multiple sources into an easy-to-read, summarized, and personalized feed.What makes Particle particularly interesting is how seamlessly they've integrated AI into the core news reading experience.

Rather than building yet another AI chatbot, they've created an intuitive news app where the AI works behind the scenes to deliver better summaries and insights.In this conversation, Sara shares valuable lessons about building AI products that actually work for customers, including:• How they approach quality and trust when using AI to summarize news

• Their practical process for developing and improving AI features
• Key learnings about evaluation pipelines and prompt engineering
• Insights on working with publishers in the AI eraTune in to hear Sara's perspective on the future of AI in media and her advice for other founders building in this rapidly evolving space.

 Whether you're a product manager, engineer, or just curious about the intersection of AI and media, this episode offers actionable insights you can apply to your own work.

Check out Particle at particlenews.ai!

Join us for an insightful conversation with Eliza Cabrera (Principal Product Manager) and Beau Lyddon (Principal Engineer) of Workday as they share their journey implementing generative AI at enterprise scale. As one of the first major enterprise software companies to roll out GenAI features, Workday offers valuable lessons for product and engineering teams navigating this technology.

Learn how Workday approached everything from their first MVP features to scaling AI across their platform, including:

• How they identified the right initial use cases and measured success
• Their approach to prompt engineering, model selection, and RAG architectures  
• Managing enterprise customer expectations around data privacy and compliance
• Creating an AI playbook to help teams across the company adopt GenAI
• Balancing innovation with enterprise-grade reliability

This episode offers practical insights for anyone working to bring generative AI capabilities to enterprise software products. Eliza and Beau share candid perspectives on what worked, what didn't, and how to think about scaling AI responsibly in complex enterprise environments.

Whether you're just getting started with GenAI or working to scale it across your organization, this conversation provides valuable lessons from leaders who have successfully navigated these challenges at one of the world's largest enterprise software companies.

• Some of the limitations of LLMs, and what to do about them — especially when it comes to summarizing lots of content (tl;dr - long context windows don’t solve everything)
• How they’ve developed their eval suite through a practical iteration process among in-house finance experts, product, and engineering
• Thoughts on staffing AI engineering teams, including what he’s seen work to get strong software engineers up to speed working with LLMs

In this episode, Freeplay co-founder & CEO, Ian Cairns sits down with Jeff Seibert, co-founder & CEO of Digits to discuss the practical applications of AI in the finance world and the lessons learned from using AI in production.

You can read a full recap with show notes here.

Jeff emphasizes the importance of choosing the right type of AI model for each use case and highlights the limitations of generative AI models. He also shares examples of how AI has improved the product experience at Digits, including predictive models for AI bookkeeping, similarity models for transaction analysis, document extraction models for invoice processing, and autonomous agents for researching unknown transactions. We also dig into the challenges of closing the gap between AI accuracy and human expertise in accounting. Digits uses generative AI to help with transaction categorization and financial reporting. 

Digits is focused on solving customer problems and uses AI as a powerful tool to deliver value. Give the episode a listen and share your feedback!