Just Now Possible

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.

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.

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.

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.

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.