How AI products come to life—straight from the builders themselves. In each episode, we dive deep into how teams spotted a customer problem, experimented with AI, prototyped solutions, and shipped real features. We dig into everything from workflows and agents to RAG and evaluation strategies, and explore how their products keep evolving. If you’re building with AI, these are the stories for you.
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Is This Okay? How Override Labs Built a Safety-First AI Consent Coach for Teen Boys
Episode 28
Thursday, June 25, 2026 • Duration 55:09
What if AI could help prevent sexual assault before it happens — without tracking users, judging them, or handing them a verdict?
In this episode of Just Now Possible, Teresa Torres talks with Priya Nakra (Founder and Product Lead) and Olivia Rowley (AI Advisor and Board Member) of Override Labs, a nonprofit building technology to prevent gender-based violence. Their flagship product, *Is This Okay?* (ITO), gives teenage boys a private, judgment-free space to reflect on ambiguous sexual scenarios — with AI guidance grounded in clinical research and motivational interviewing.
Priya and Olivia share how they built ITO from scratch: scraping Reddit to validate the need, partnering with a licensed therapist to design the eval rubric, and building a risk classification system that runs *before* Claude is ever invoked. Every design decision — from skipping account creation to removing the concept of a "green light" response — was made with one goal: never let the product be used to justify harm.
You'll hear how they defined a "South star" instead of a North star, how clinical expertise shaped the AI's tone and structure, and why a nonprofit context unlocks design choices that growth-focused companies simply can't make. It's a masterclass in purpose-built AI product development when the goal isn't scale — it's prevention.
Beyond Black Box Scores: How Musubi Trains Custom AI for Trust and Safety Teams
Episode 27
Thursday, June 11, 2026 • Duration 01:12:40
What do you do when off-the-shelf moderation scores aren't good enough—and the alternative is paying human contractors to spend their days reviewing traumatizing content at scale?
In this episode of Just Now Possible, Teresa Torres talks with Nikki Marinsek (Data Scientist), Brian McCaffrey (Software Engineer), and Dan Means (Machine Learning Engineer) from Musubi, an AI-native trust and safety toolkit for content platforms. Musubi builds custom-trained ML models and LLM-powered moderation tools that adapt to each platform's unique policies—from dating apps to social networks to AI inference endpoints.
They walk through the full journey: training the first prototype on tabular data, discovering their AI was sometimes catching things human moderators missed, and building a policy optimizer that uses agentic flows to help teams iterate on their moderation policies without needing a data scientist in the room.
You'll hear how they balance latency, accuracy, and cost for clients handling hundreds of millions of actions per month, why pushing eval tools directly to customers is their core product strategy, and what's next as they build flexible agentic orchestration for non-technical trust and safety teams.
Building Lorikeet: How AI Humility and a Dual-Agent Architecture Are Redefining Customer Support
Episode 26
Thursday, May 28, 2026 • Duration 01:08:15
What does it take to build an AI customer support agent that actually knows when it can't help — and says so?
In this episode of Just Now Possible, Teresa Torres talks with Jamie Hall (Co-founder & CTO), Xharmagne Carandang (Product Engineer), and Rona Wang (Product Engineer) of Lorikeet, a startup building AI customer support concierge agents for businesses in regulated industries. Lorikeet's vision: an agent that responds like the best customer support you've ever had — one that knows you, gets things fixed, and hands off gracefully when it's out of its depth.
The team spent months exploring the wrong ideas — reflection tools, information dashboards — before a healthcare startup pulled them toward the real problem: just help us clear the inbox. Their earliest prototype was a command-line script delivering results via CSV. Today, Lorikeet runs two agents: a Concierge that handles customer tickets end-to-end, and a Coach that helps customers configure, test, and continuously improve it.
You'll hear how they built customer-configurable guardrails (and why a cannabis company's support tickets broke their first approach), designed a "resolution in the loop" pattern for human-AI collaboration, and are now flipping the configuration workflow so customers define what good looks like before they ever write a standard operating procedure.
Building Rhea's Factory: How AI-Designed Enzymes Could Finally Solve Plastic Recycling
Episode 25
Thursday, May 14, 2026 • Duration 01:10:22
Only 10% of the plastic we manufacture gets recycled. We've been trying to solve this for a hundred years using the same mechanical and chemical tools that created the problem. What if biology—specifically, engineered enzymes—is the missing piece?
In this episode of Just Now Possible, Teresa Torres talks with Arzu Sandıkçı (co-founder and CEO) and Mert Topcu (co-founder) of Rhea's Factory, a startup using engineered enzymes and AI to achieve what mechanical recycling can't: breaking plastic all the way back to its original molecular building blocks.
Arzu brings a background in molecular biology and enzyme engineering. Mert brings 20 years in tech, including a decade at Google as a product manager. Together, they've built an AI platform that uses protein language models, multi-step agentic pipelines, and proprietary wet lab data to design novel enzymes that deconstruct plastic polymers into their original monomers—selectively, at low temperatures, and at industrial scale.
You'll hear how they evolved from a human-orchestrated pipeline to an agentic AI scientist, why they sometimes *want* the model to hallucinate, and what it means to explore an enzyme design space that makes everything nature has ever evolved look like a tiny dot.
Building AI Employees for Hospitality: How AITropos Takes Orders Where Customers Already Are
Episode 24
Thursday, April 30, 2026 • Duration 01:07:41
What does it take to build an AI that can take a food order over WhatsApp — correctly, every time, fast enough that customers can't tell it's not a person? That's the core challenge Santi Marchiori and Juan Haedo set out to solve at AITropos, a company building AI employees for the hospitality industry.
In this episode of Just Now Possible, Teresa Torres talks with Santi Marchiori (CEO) and Juan Haedo (CTO) of AITropos about how they built an AI order-taking agent that handles the full flow — menu recommendations, modifiers, delivery zones, payment links, and status updates — entirely inside WhatsApp. They went through three product iterations to get there: first a hardware device for waiters, then a waiter-facing app, and finally a customer-facing conversational agent powered by a tools-based architecture designed for speed and reliability.
You'll hear how they solved the core technical challenge of translating non-deterministic human conversation into structured POS-compatible order data, why they chose tools over MCP for agent architecture, how they pre-inject product context to cut latency before the agent ever makes a tool call, and why they test with thousands of agent-simulated customer conversations overnight before deploying to any real venue.
Building Todoist Ramble: How Doist Turned Voice Braindumps into Real-Time Task Capture
Episode 23
Thursday, April 16, 2026 • Duration 01:00:40
How do you turn a rambling stream of consciousness into a clean task list — while the person is still talking? That's the core challenge Doist solved with Ramble, a voice-to-task feature inside Todoist that uses live audio AI to capture tasks in real time, no transcription step required.
In this episode of Just Now Possible, Teresa Torres talks with Ernesto Garcia (Front-end Product Engineer), Thomas Jost (Backend Software Engineer), and Hugo Fauquenoi (Product Manager) from Doist about how they built Ramble — Todoist's first pure AI feature. What started as a two-to-three month AI exploration phase became one of the most technically deliberate features they've shipped: a Gemini-powered pipeline that makes tool calls while the user is still speaking, surfacing tasks on screen in real time without any text output from the model.
You'll hear how they designed around the "brain dump" behavior they found in user research, why they chose direct context injection over RAG for project and label matching, the surprising complexity of date handling in a live audio pipeline, and how they built a multi-language eval system using real employee recordings across 35 countries. It's a detailed look at the discipline of keeping AI features simple, constrained, and genuinely useful.
Building Banani: How a Canvas-First AI Designer Is Raising the Floor on Product Design
Episode 22
Thursday, April 2, 2026 • Duration 01:10:10
What if the future of product design isn't about replacing designers — it's about giving every team access to one? For solo founders, stretched design teams, and early-stage startups, great UX has always been out of reach. Banani is trying to change that by building an AI product designer that doesn't just generate code — it generates design.
In this episode of _Just Now Possible_, Teresa Torres talks with Vlad Solomakha (CEO & Co-founder), Vova Parkhomchuk (CTO & Co-founder), and Vlad Ostapovats (Founding Growth) about how they built Banani from a Figma plugin proof-of-concept into a canvas-first AI design tool generating hundreds of thousands of designs per week. Vlad Solomakha brings a decade of design experience to the product — and a very specific vision of what it means for AI to produce beautiful, tasteful design rather than average, undifferentiated UI.
You'll hear how they engineered their agent to handle parallel screen edits, manage per-screen context across canvases with hundreds of frames, and make surgical edits without regenerating entire screens. They dig into the "gulf of specification" — the mismatch between how designers think visually and how agents understand text — and what they're building to close it.
It's a detailed look at what it takes to build an AI-native design tool that puts the designer in the driver's seat while letting the agent handle the production work.
Building Agent Studio: How Medable Is Using Agentic AI to Accelerate Clinical Trials
Episode 21
Thursday, March 19, 2026 • Duration 01:06:13
What if AI could help reduce the 10-plus years it takes to get a new drug to market? That's the driving ambition behind Medable's agentic platform—and the bet that led them to build Agent Studio.
In this episode of Just Now Possible, Teresa Torres talks with four members of the Medable team: Luke Bates (Product Leader, Agent Studio), Jen Brown (Product Manager), Matt Schoolfield (Product Designer), and Fiachra Matthews (Principal Architect). Together they share how Medable—a clinical trial platform used by global pharmaceutical companies—built Agent Studio, a no-code/low-code platform for configuring and deploying agents across the clinical trial lifecycle.
You'll hear about the two agents they've built on top of it: an ETMF agent that automates document classification across 80,000-plus documents per year, and a CRA agent that monitors patient safety and data quality across 13 different clinical systems. The conversation goes deep on the architecture behind it all—how they handle RAG and context management at scale, why they built custom MCPs with an authentication layer, how they designed evals for a regulated GXP environment, and what human-in-the-loop really looks like when clinical decisions are on the line.
It's a rare look inside an enterprise AI platform built for one of the most regulated industries in the world—and a team that's still figuring it out in real time.
Building GitHub for Product Management: How Momental Uses AI to Find Merge Conflicts in Strategy
Episode 20
Thursday, March 5, 2026 • Duration 01:04:46
What if an AI could spot the moment two product teams start pulling in opposite directions -- before it derails a quarter?
In this episode of Just Now Possible, Teresa Torres talks with Matthias and Charlotte Kleverud, co-founders of Momental, about their vision for building what they call "GitHub for product management." Momental ingests documents, meeting transcripts, and voice recordings across an organization, then uses AI agents to map them into a structured context layer—a set of interconnected trees covering goals, decisions, learnings, and who's doing what. When it finds a conflict—say, one team betting on retention while another is prioritizing conversion—it surfaces the misalignment for humans to resolve, just like a merge conflict in code.
You'll hear how their journey started in 2022 with GPT-3, pivoted through a multi-agent team that exposed the real problem (agents need aligned context, too), and landed on an OODA-loop-driven document processing agent that builds and maintains a living knowledge graph. It's a detailed look at how domain-specific context modeling, human-in-the-loop conflict resolution, and self-improving agents come together to tackle one of the hardest problems in product organizations: keeping everyone aligned.
Building AI Sales Reps: How ShowMe Orchestrates Voice, Video, and Multi-Agent Workflows to Close Deals
Episode 19
Thursday, February 19, 2026 • Duration 01:03:58
What happens when you treat an AI agent not as a chatbot, but as a full teammate on your sales team -- one that can jump on video calls, demo your product, make phone calls, and follow up over days?
In this episode of Just Now Possible, Teresa Torres talks with Yuri Vela Tulupov (Co-Founder and CEO) and Quique Gomez (Co-Founder and Lead Product Engineering) from ShowMe, an AI-native startup building digital sales reps for companies selling through inbound channels. Founded in April 2025, ShowMe has already built a sophisticated multi-agent system where conversation agents handle live voice and video interactions, evaluator agents score every call for quality and sentiment, and creator agents ingest customer documentation to build tailored sales playbooks -- all coordinated by a workflow layer that manages the full lead-to-close journey.
You'll hear how they decompose a single sales conversation into multiple specialized sub-agents to manage latency and model limitations, why adding a realistic avatar (via HeyGen) dramatically changed how prospects engaged with the AI, and how customer-driven eval loops—where every conversation is reviewed early on and gradually reduced to about 5%—keep quality high for revenue-critical interactions. It's a detailed look at what it takes to build agents that don't just talk, but actually sell.