Just Now Possible

Teresa Torres

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.

  1. vor 4 Tagen

    Creating Aha! Builder: Concept to Code, No Engineers Required

    What does it take to build an AI app builder specifically for product managers—not engineers—inside an already crowded market? In this episode of Just Now Possible, Teresa Torres talks with Brian De Haaff (CEO and Co-Founder), Chris Waters (CTO and Co-Founder), and Sarah Moisan-Thomas (Senior Product Manager) about Aha! Builder, Aha!'s AI-powered app builder built for product managers. Aha! is a fully bootstrapped, profitable, remote-first company that has served product teams for over 13 years without a single salesperson. They walk through Aha!'s five-step product discovery framework—spark, paper prototypes, proof of concept, early access, and general availability—that led them to Builder. They share why their first version, built on containerized Ruby on Rails infrastructure, proved too wasteful to scale, and how they rebuilt it on a single-instance, multi-tenant architecture using V8 isolates. Along the way, they explain their approach to deterministic guardrails for things like authentication, SSO, and databases, and how a multi-phase, multi-agent pipeline generates a design system, a prototype, and a working application in minutes. You'll hear how they think about the line between what AI should build and what should stay deterministic, how they handle enterprise concerns like SSO and PII, and why they believe knowing what to build—not how to build it—is where product managers will increasingly add value.

    Creating Aha! Builder: Concept to Code, No Engineers Required

  2. 23. Juli

    Building AI for Women's Health: How Hertility Combined Bayesian Diagnosis and Scan Automation

    How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause. Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: Gyn.AI, a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever reviews it. You'll hear how the team guards against automation bias, builds clinician trust through transparency, minimizes PII before it ever reaches a model, and treats healthcare regulation as a design constraint from day one rather than a last-minute scramble. It's a detailed look at what it takes to bring AI into one of the most sensitive, tightly regulated corners of healthcare.

    Building AI for Women's Health: How Hertility Combined Bayesian Diagnosis and Scan Automation

  3. 9. Juli

    From COVID Pivot to AI World Building: How Snapbar Reinvented the Photo Experience

    What does it take to reinvent a 14-year-old company—not once, but twice? In this episode of Just Now Possible, Teresa Torres talks with Sam Eitzen (Co-founder & CEO), Joe Eitzen (Co-founder & CPO), and Patrick Ellis (CTO) of Snapbar about one of the most unexpected pivots in the generative AI era. What started as a wedding photo booth side hustle became a national events company—and then COVID wiped out the entire business overnight. What the team built in response now looks so AI-native that Teresa assumed Snapbar was a new startup. You'll hear how they went from physical photo booths to a cross-platform virtual product built on WebRTC in spring 2020, and then—pushed by declining repeat business—dove deep into Stable Diffusion, custom LoRA fine-tunes on H100/H200 GPUs, and eventually a reasoning-model-powered generative image and video pipeline. Along the way, they built an agent orchestration framework for their engineering process using Claude Code and Codex, and are now giving brand customers the ability to "vibe code" within the Snapbar platform itself. If you've ever wondered what applied AI looks like when you combine 14 years of industry knowledge, photography expertise, and relentless curiosity-led self-education, this episode shows exactly that.

    From COVID Pivot to AI World Building: How Snapbar Reinvented the Photo Experience

  4. 25. Juni

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

    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.

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

  5. 11. Juni

    Beyond Black Box Scores: How Musubi Trains Custom AI for Trust and Safety Teams

    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.

    Beyond Black Box Scores: How Musubi Trains Custom AI for Trust and Safety Teams

  6. 28. Mai

    Building Lorikeet: How AI Humility and a Dual-Agent Architecture Are Redefining Customer Support

    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 Lorikeet: How AI Humility and a Dual-Agent Architecture Are Redefining Customer Support

  7. 14. Mai

    Building Rhea's Factory: How AI-Designed Enzymes Could Finally Solve Plastic Recycling

    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 Rhea's Factory: How AI-Designed Enzymes Could Finally Solve Plastic Recycling

  8. 30. Apr.

    Building AI Employees for Hospitality: How AITropos Takes Orders Where Customers Already Are

    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 AI Employees for Hospitality: How AITropos Takes Orders Where Customers Already Are
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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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  • Erstellt von
    Teresa Torres
  • Jahre aktiv
    2025 - 2026
  • Folgen
    31
  • Bewertung
    Unbedenklich
  • Copyright
    © All Rights Reserved | Product Talk
  • Sendungswebsite
    Just Now Possible

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