Deployed: The AI Product Podcast

Loïc Houssier, CTO at Superhuman Mail, builds product-first, high-quality email experiences with generative AI. He discusses AI features like auto-drafts, semantic search, and personalized triage. Short takes cover building evals from the hardest inbox queries, mixing models, privacy-safe testing, and using coding agents and a quarterly quality week to keep a luxury bar.

On this episode of Deployed we talk with Lijuan Qin, Head of Product for Zoom AI, about how her team is moving beyond AI meeting transcriptions and note-taking to a mission of agents helping from "conversation to completion." That's how Lijuan describes her vision of the future for Zoom AI, where AI doesn't just summarize your meetings, it actually follows through on the work that comes after.Lijuan has a PhD in AI and spent 20 years at Microsoft working on NLP and video understanding before joining Zoom. She brings a long-arc perspective on what's changed and what hasn't in AI, and shares how her team thinks about building an AI companion that acts more like a team member than a search engine.Key insights for builders include:* Why high engagement with an AI product can be a negative signal. i.e. if users keep going back and forth with your AI, the product might be failing them ("you got it wrong! try again").* How Zoom measures AI quality by *output* value and task completion, instead of usage metrics or individual response accuracy* Their "AI-first, intent-driven" approach: starting from what the user needs to get done, not which tool to use* How they personalize AI features in stages: role-based outputs first, then memory, then live conversation context, rather than trying to build something like a full "digital twin" on day one* A concrete example: Drafting different kickoff documents for each meeting attendee that is personalized for the priorities of their role (PM vs. engineer vs. CTO)* Why transparent decision frameworks let big organizations experiment fast without approval loops* How the Zoom AI team balances speed and enterprise trustLinks from our conversation:* Zoom AI Companion: https://ai.zoom.us* Find Lijuan on LinkedIn: https://www.linkedin.com/in/lijuanqin/* Freeplay (that's us): https://freeplay.ai

Kevin Stanton, distinguished engineer who built Trellis for social listening at Sprout Social, walks through running agents over billions of posts. He explains why chat UI helps seed eval data and why MCP/tool-calling fit their architecture. He covers scaling signals, collapsing costly tool chains, building eval pipelines from traces, and shifting teams toward fast, multidisciplinary agent development.

On this episode of Deployed, we sit down with Shuhao Zhang, co-founder and CPO of TinyFish, on launch day for Mino (mino.ai) - an enterprise web agent platform that handles reasoning, multi-step execution, and parallel browser sessions at scale.Shuhao shares his vision for the "operational web" where AI agents become the primary operators of the web, unlimited by human constraints. He shares a live demo and customer stories, like working with Google to connect tens of thousands of Japanese hotels to consumers, and with ClassPass to maintain up-to-date pricing across 30,000 wellness studios.Key technical insights for builders include:- Why public benchmarks for web agents are saturated today, and you need your own evals- How they built infrastructure to replay sessions like a "time machine" for data iteration- Why they fix issues through data improvement rather than custom patches / rule-based systemsLearn more:- Minnow: mino.ai- Tiny Fish: tinyfish.ai- AgentQL: agentql.com- Find Shuhao on LinkedIn (https://www.linkedin.com/in/shuhao/) and Twitter (https://x.com/shuhao_friday)

On this episode of Deployed, we sit down with Ajit Varma, VP of Product at Mozilla Firefox, and former product leader at Google, Meta, Square, and WhatsApp. Ajit brings a unique perspective: while venture-backed AI browsers race to build what increasingly look like walled gardens, Mozilla has spent months quietly shipping privacy-first AI features that put user choice above everything else.It's a thoughtful conversation with candid insights into why Firefox offers multiple AI chat providers in their sidebar instead of forcing users into a single model (and how this philosophical stance shapes every technical decision they make), the reality that privacy-preserving AI requires completely rethinking architecture, and why their AI features prioritize on-device processing whenever possible. We also get into the technical challenges that come with balancing local and cloud AI (users want SOTA quality but also want their data to stay private), the business model constraints of being a nonprofit foundation competing against VC-funded competitors who can burn cash on flashy features, and why Mozilla believes keeping the web open matters more than winning the AI browser wars. Whether you're interested in privacy and the open web or just trying to understand what it takes to integrate cutting-edge AI into existing products, Ajit's perspective offers practical lessons from someone navigating a challenging balancing act.

Surojit Chatterjee, CEO and founder of Ema, shares his journey building enterprise AI employees since early 2023. He emphasizes the critical difference between treating AI as employees rather than agents, impacting feedback and evaluation. Surojit discusses the failure of many AI projects due to automation of broken processes and promotes multi-agent systems as essential for managing enterprise complexity. He also highlights the importance of redesigning workflows, sustainable scalability, and effective training for business users to manage AI effectively.

In this episode of Deployed, we sit down with Ryan Carson, Builder in Residence at Amp (Sourcegraph's coding agent) and founder of Treehouse, which taught over a million people to code. Ryan brings a rare dual perspective: he's both building his own company with AI tools and helping create enterprise-grade AI coding infrastructure.Ryan gives us refreshingly honest insights into the hyper-competitive coding agent landscape, why traditional evals don't work for open-ended coding tasks, and the "dirty secret" that most quality decisions still come down to "dev vibes." We dive into the technical reality of competing with well-funded teams, his systematic framework for building with AI agents (which has 5,000+ GitHub stars), and why success isn't just about model capabilities — it's about solving real developer problems through obsessive attention to user experience.Whether you're building AI products or trying to understand what it really takes to compete in this space, Ryan's grounded perspective cuts through the hype with practical lessons from the trenches.Retry

Kelly Schaefer, recognized as one of the Top 100 Women in AI and Product Director at Google Labs, shares insights on turning innovative ideas into real-world AI products. She discusses the importance of evaluation scorecards and the balance between speed and perfection in product launches. The conversation highlights promising tools like NotebookLM and coding assistant Jules, which enhance productivity and user engagement. Kelly emphasizes the evolving roles of PM and UX in AI development, advocating for 'dot connectors' who bridge diverse domains.

Kwindla Hultman-Kramer, co-founder of Daily and creator of Pipecat, shares his extensive expertise in voice AI, built since its early days. He discusses the current inflection point for voice agents transitioning to real-world applications with tangible ROI. Kwindla highlights best practices for infrastructure, unveiling why WebRTC outperforms WebSockets. He also delves into the limitations of speech-to-speech models and the importance of solid text evaluations, providing valuable tactical advice for developers navigating the journey from prototypes to production.

On this episode we talk with Nathan Sobo, co-founder and CEO of Zed, the high-performance code editor that's reimagining how AI agents can improve developer workflows. Nathan shares lessons from building one of the most natural agentic coding experiences available, including why investing up front to automate the feedback loop to improve the quality of AI systems is worth it. He also shares some great product design insights that go beyond the code editor -- including how they were able to weave an AI agent into the UX of their already-collaborative product (just like a human collaborator), and how their "subtle mode" for code completion has helped win over AI-skeptical developers. Whether you're building coding tools or another AI product, this conversation is a fun listen for anyone who cares about crafting great products and incorporating AI in smart ways.