Just Now Possible

Episodet

• Is This Okay? How Override Labs Built a Safety-First AI Consent Coach for Teen Boys 25.06.2026 55min

  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 11.06.2026 1h 12min

  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 28.05.2026 1h 8min

  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 14.05.2026 1h 10min

  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 30.04.2026 1h 7min

  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 16.04.2026 1h

  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 02.04.2026 1h 10min

  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 19.03.2026 1h 6min

  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 05.03.2026 1h 4min

  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 19.02.2026 1h 3min

  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.
• Building Earmark: How a Two-Person Team Turned Meetings into Finished Work 05.02.2026 1h

  What if your meetings could actually produce the artifacts you need—specs, tickets, slides—before the call even ends? In this episode of Just Now Possible, Teresa Torres talks with Mark Barbir (CEO) and Sanden Gocka (Co-Founder), the co-founders of Earmark, about building a productivity suite that turns unstructured conversations into finished work in real time. Unlike generic AI notetakers that produce summaries nobody reads, Earmark runs multiple agents in parallel during your meetings—translating engineering jargon, drafting product specs, even spinning up prototypes in Cursor or V0 while you're still talking. You'll hear how they pivoted from an Apple Vision Pro presentation coaching tool to a web-based meeting assistant, why their ephemeral (no-storage) architecture became a feature for enterprise sales, and the technical challenges of making real-time AI affordable—from $70 per meeting down to under a dollar through prompt caching. They also dig into why vector search falls short for analysis questions and how they're building agentic search to find insights across months of meetings. Whether you're a PM drowning in follow-up work or a builder curious about real-time AI architectures, this conversation offers a detailed look at what it takes to ship an AI product that people can't imagine working without.
• When Trust Is Everything: Building AI for Physicians at Healio 22.01.2026 49min

  How do you build an AI product for an audience that can't afford to be wrong—and won't trust you until you prove it? In this episode of _Just Now Possible_, Teresa Torres talks with three leaders from Healio—Jennifer Deal (SVP of Product Development), Casey Utley (Senior UX Designer), and Matthew Skepner (VP of Technology)—about how their 125-year-old medical publishing company built Healio AI, an AI-powered assistant that helps physicians prepare for patient care. They share how a survey of 300 healthcare professionals shaped their early assumptions, why physicians surprised them by asking for help with patient communication rather than diagnostics, and how they built a working prototype in a single weekend using Cursor. You'll hear how they combined RAG with hybrid search across trusted sources like PubMed, designed citation UX that physicians actually trust, and set up eight LLM judges alongside real physician feedback to evaluate response quality. If you're building AI for a high-stakes domain where trust, accuracy, and transparency matter more than speed, this conversation is packed with practical lessons.
• Building Tendos AI: How an Agent Swarm Turns Construction Emails into Quotes 15.01.2026 1h 5min

  When a construction company receives a bid request, someone has to open that email, parse the attached PDF (sometimes 1,800 pages describing an entire building), figure out which products are relevant, look up pricing, and draft a quote—all before the deadline. It's tedious, error-prone, and surprisingly manual. In this episode of _Just Now Possible_, Teresa Torres talks with Daniel Kappler (CTO, Product & Design) and Matthias Hilscher (CTO, Engineering) from Tendos AI about how they're automating this entire workflow for manufacturers in the construction industry. What started as a narrow prototype matching radiator requests to product catalogs has grown into a full agentic system that handles everything from email categorization to offer generation. You'll hear how they validated the opportunity with a design partner, spent a week on-site watching users work, and built a multi-agent architecture where specialized agents collaborate—complete with a "review agent" that checks the work of other agents before anything reaches a human. They dig into why they evaluate each agent independently (not just the whole chain), why they built custom observability tools when off-the-shelf solutions fell short, and how human-in-the-loop feedback is pushing them toward a self-learning system.
• Building a Career Co-pilot for Disadvantaged Students: How Zero Gravity Bridges Knowing and Doing 08.01.2026 1h 9min

  How do you help disadvantaged students take action on opportunities they don't even know exist? In this episode of _Just Now Possible_, Teresa Torres talks with Elliot Little (Product Manager) and Dan St. Paul (Software Engineer) from Zero Gravity, a UK-based platform that helps state school students access elite career opportunities through mentoring, community, and learning pathways. They've built an AI career co-pilot that acts as an orchestrator—not an automation tool—bridging the gap between knowing what to do and actually doing it. You'll hear how they: - Started with grand visions of AI mentors and synthetic avatars, then scaled back to something simpler and more effective - Discovered that hiding the "LLM magic" backfired—students needed to feel the personalization - Built context management strategies to handle multi-month student journeys without blowing up token counts - Approached safeguarding as a first-class concern when building AI for 16-year-olds - Used application logic rather than complex RAG architectures to manage tool availability and context freshness It's a practical look at building AI products that augment human relationships rather than replace them—from a team navigating the unique challenges of educational technology.
• Automating the Full Customer Support Iceberg: How Gradient Labs Built a Multi-Agent Platform 18.12.2025 1h 1min

  What happens when a customer reports a stolen credit card? The frontline answer is simple—freeze it. But underneath lies a cascade of follow-ups: dispute filings, fraud investigations, merchant communications, and proactive outreach to gather more details. Most AI support tools handle only the tip of the iceberg. In this episode, Teresa Torres talks with Jack Taylor (Product Engineer) and Ibrahim Faruqi (AI Engineer) from Gradient Labs, an AI-native startup building agents that automate the full scope of customer support in fintech. They share how they've architected a platform with three coordinating agents—inbound, back office, and outbound—all built on a shared foundation of natural language procedures, modular skills, and configurable guardrails. You'll hear how they: - Let non-technical subject matter experts define agent behavior through natural language procedures—no coding required - Architected a state machine orchestrator that manages turns, triggers, and skill selection across long-running conversations - Built guardrails as binary classifiers with eval pipelines, tuning for high recall on critical regulatory checks - Designed an auto-eval system that samples conversations for human review to catch edge cases and build labeled datasets It's a detailed look at how one startup is moving beyond simple Q&A bots to agents that can actually take action, coordinate across workflows, and handle the messy reality of customer support.
• Building Mowie: How a Concierge Service Became an AI Marketing Platform 11.12.2025 1h 7min

  What if your small business could have a full marketing team—automated content calendars, customer segmentation, and channel-specific posts—without the headcount? In this episode of Just Now Possible, Teresa Torres talks with Chris O'Connor (CEO) and Jessica Valenzuela (Co-Founder) of Mowie, an AI marketing platform built for small and medium-sized businesses in restaurants, retail, and e-commerce. Chris and Jessica share how their hands-on experience managing marketing for overwhelmed business owners at a previous company led them to build Mowie—first as a concierge service, then as a fully automated AI product. They walk through their document hierarchy approach: how Mowie crawls the web to build a "dossier" about each business, infers customer segments and marketing pillars, and generates quarterly content calendars with channel-specific posts. You'll hear about the technical challenges of structuring unstructured data, the evolution from rigid schemas to loosely structured markdown, and how they use customer feedback—from calendar approvals to regeneration requests—as their primary evaluation signal. Whether you're building AI products that synthesize messy real-world data or figuring out how to keep humans in the loop without overwhelming them, this conversation offers practical lessons from two founders who built their product by doing the work first.
• From Prototype to Production: How Perk Built a Voice AI Agent That Makes 10,000 Calls a Week 04.12.2025 54min

  What happens when you combine a real customer problem, a no-code prototype, and a team willing to listen to every single call? In this episode of _Just Now Possible_, Teresa Torres talks with Steven Payne (Product Manager), Gabriel Stock (Senior Engineering Manager), and Philipe Steiff (Senior Software Engineer) from Perk—a company that helps businesses eliminate "shadow work" like travel booking and expense management. They share how they built a voice AI agent that calls hotels to verify virtual credit card payments, preventing travelers from arriving to find their rooms unpaid. What started as a hackathon experiment in Make.com became a production system handling over 10,000 calls per week across multiple languages. Along the way, the team learned hard lessons about prompt engineering for voice (numbers, pronunciation, and a very "Karen-like" first version), how to break a single monolithic prompt into structured conversation stages, and why listening to actual calls beats any amount of theorizing. You'll hear how they: - Built a working prototype without writing a single line of backend code - Structured the call into discrete stages (IVR, booking confirmation, payment) to improve reliability - Created two eval systems: one for call success classification, another for conversational behavior - Scaled from five calls a day to tens of thousands per week while maintaining quality This is a detailed look at building AI for real-time human interaction—where the stakes are high and the feedback is immediate.
• Building an AI Sleep Coach: How Rest is Making CBTI Principles Accessible to DIY Sleep Hackers 20.11.2025 1h 6min

  What if you could get personalized sleep coaching—inspired by the same principles that cost thousands of dollars and have year-and-a-half waitlists—through a voice AI that checks in with you every morning? In this episode of Just Now Possible, Teresa Torres talks with Martin Siniawski (CEO and co-founder) and Ignacio (CTO) from Rest about how they built an AI sleep coach inspired by Cognitive Behavioral Therapy for Insomnia (CBTI) principles. The journey started when they noticed users of their podcast app were listening to content to fall asleep, explored sleep audio solutions, and eventually pivoted to an AI-powered voice coach when LLMs emerged. They share how they evolved from basic chatbots to a sophisticated voice-first system with memory, dynamic agendas, and RAG—all while navigating the tricky line between wellness and medical products. Their "one bite of the apple at a time" approach to building AI offers practical lessons for teams tackling complex, personal AI products.
• Turning Vendor Chaos into Answers: How Xelix Built an AI Helpdesk 13.11.2025 53min

  Accounts payable inboxes can see 1,000+ vendor emails a day. Xelix's new Helpdesk turns that chaos into structured tickets, enriched with ERP data, and pre-drafted replies—complete with confidence scores. In this episode, Claire Smid (AI Engineer), Emilija Gransaull (Back-End Tech Lead), and **Talal A.** (Product Manager) walk us through how they scoped the problem, prototyped with “daily slices” (Carpaccio-style), and built a retrieval-first pipeline that matches vendors, links invoices, and drafts accurate responses—before a human ever clicks “send.” We dig into tricky bits like vendor identity matching, Outlook threading, UX pivots from “inbox clone” to ticket-first views, and the metrics that prove real impact (handling time, stickiness, auto-closed spam). We close with what’s next: targeted generation, multiple specialized responders, and more agentic routing.
• When AI Becomes Your SRE: How Incident.io Is Automating Incident Response 06.11.2025 1h 8min

  When your site goes down, every second counts. For years, Incident.io has helped engineering teams coordinate through chaos—getting the right people in the room, keeping stakeholders informed, and restoring order fast. Now, they’re building something new: an AI SRE that can actually help diagnose and respond to incidents. In this episode, Teresa Torres talks with Lawrence Jones (Founding Engineer) and Ed Dean (Product Lead for AI) about how their team is teaching AI to think like a site reliability engineer. They share how they went from simple prototypes that summarized incidents to a multi-agent system that forms hypotheses, tests them, and even drafts fixes—all from within Slack. You’ll hear how they: - Identify which parts of debugging can safely be automated - Combine retrieval, tagging, and re-ranking to find relevant context fast - Use post-incident “time travel” evals to measure how well their AI performed - Balance human trust and AI confidence inside high-stakes workflows This is a masterclass in designing AI systems that think, reason, and collaborate like expert teammates.