Just Now Possible

Afleveringen

1. Building an AI Voice Agent for 360 Degree Feedback: How Your360 AI Uses State Machines, Evals, and Layered Synthesis

   15 oktober 2026 · 1 uur 8 min

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   What does it take to build an AI voice agent that people will talk to for 44 minutes about their own blind spots? Your360 AI found out, one call at a time. Lees meer

   In this episode of Just Now Possible, Teresa Torres talks with Jared Goralnick (Co-Founder and CEO), Eda Goksel Jackson (Head of Design), and Alex Krasun (AI Engineer) at Your360 AI. Their product brings interview-based 360 degree feedback, once reserved for the C-suite, to anyone. A voice AI coach named Tam runs the self-discovery call, interviews each feedback provider, and walks the recipient through their report.

   They get specific about what it took: replacing a single prompt with state machines so Tam doesn't lose the thread, moving from Retell to LiveKit to control every layer of a cascading voice stack, and running AI evals with personas built from calls that failed. Then comes the hard part: synthesis that is accurate, corroborated, and genuinely anonymous.

   You'll hear how they balance observability against sensitive data, why reasoning models are a poor fit for voice, and how structural and substantive eval passes catch both outright mistakes and missed patterns.

2. Generating Opportunity Solution Trees with AI: How Vistaly Rebuilt Its Product Around Interview Synthesis, Evals, and Re

   1 oktober 2026 · 1 uur 20 min

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   What happens when you hand your opportunity solution tree to an AI? Vistaly rebuilt its entire product to find out—and the agents were the easy part. Lees meer

   In this episode of Just Now Possible, Teresa Torres talks with Matt O'Connell (Co-Founder and CEO), CP Dehli (Co-Founder), and Steve Klein (Co-Founder) at Vistaly, a company Teresa partners with and builds for. V1 was a purpose-built canvas for continuous discovery. V2 is a ground-up rewrite: upload customer interviews, get a snapshot of each, and let an agentic workflow draft and update your tree.

   They get specific about what broke. A chat-based synthesis experiment users found too slow. A "house of cards" problem where one badly framed opportunity corrupts every layer above. Teresa walks through the AI evals work behind one customer complaint—"I'll have to come back and clean up this branch"—including an LLM as a judge she couldn't calibrate and the orchestration change that fixed what no prompt could.

   You'll hear why change sets had to be taught to the agent as the rules of a game instead of diffed at the end, how data residency pushed them onto Bedrock, and why their hardest problem isn't output quality—it's helping people comprehend what changed.

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

   17 september 2026 · 1 uur 7 min

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   What does it take to build an AI app builder specifically for product managers—not engineers—inside an already crowded market? Lees meer

   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.

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

   23 juli 2026 · 1 uur 4 min

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   How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? Lees meer

   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.

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

   9 juli 2026 · 1 uur 14 min

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   What does it take to reinvent a 14-year-old company—not once, but twice? Lees meer

   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.

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

   25 juni 2026 · 55 min

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   What if AI could help prevent sexual assault before it happens — without tracking users, judging them, or handing them a verdict? Lees meer

   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.

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

   11 juni 2026 · 1 uur 13 min

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   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? Lees meer

   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.

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

   28 mei 2026 · 1 uur 8 min

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   What does it take to build an AI customer support agent that actually knows when it can't help — and says so? Lees meer

   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.

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

   14 mei 2026 · 1 uur 10 min

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   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? Lees meer

   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.

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

    30 april 2026 · 1 uur 8 min

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    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. Lees meer

    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.

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