Just Now Possible

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

Trainline—the world’s leading rail and coach platform—helps millions of travelers get from point A to point B. Now, they’re using AI to make every step of the journey smoother. In this episode, Teresa Torres talks with David Eason (Principal Product Manager) Billie Bradley (Product Manager), and Matt Farrelly (Head of AI and Machine Learning) from Trainline about how they built Travel Assistant, an AI-powered travel companion that helps customers navigate disruptions, find real-time answers, and travel with confidence. They share how they: - Identified underserved traveler needs beyond ticketing - Built a fully agentic system from day one, combining orchestration, tools, and reasoning loops - Designed layered guardrails for safety, grounding, and human handoff - Expanded from 450 to 700,000 curated pages of information for retrieval - Developed LLM-as-judge evals and a custom user context simulator to measure quality in real-time - Balanced latency, UX, and reliability to make AI assistance feel trustworthy on the go It’s a behind-the-scenes look at how an established company is embracing new AI architectures to serve customers at scale.

How do you use AI to help city leaders truly hear their residents? In this episode, Teresa Torres talks with Noa Reikhav (SVP of Product), Andrew Therriault (VP of Data Science), and Shota Papiashvili (SVP of R&D) from Zencity, a company that powers government decision-making with community voices. They share how Zencity brings together survey data, 311 calls, social media, and local news into a unified platform that helps cities understand what people care about—and act on it. You’ll hear how the team built their AI assistant and workflow engine by being thoughtful about their data layers, how they combined deterministic systems with LLM-driven synthesis, and how they keep accuracy and trust at the core of every AI decision. It’s a fascinating look at how modern AI infrastructure can turn noisy, messy civic data into clear, actionable insight.

What if your next teammate was an AI coworker — one that could answer support tickets, process invoices, or even draft your next email — and your _non-technical_ colleagues could teach it how to do those tasks themselves? In this episode, host Teresa Torres talks with Seyna Diop (CPO), Job Nijenhuis (CTO & Co-founder), and Christos C. (Lead Design Engineer) of Neople, a company creating “digital coworkers” that blend the reliability of automation with the empathy and flexibility of AI. They share how Neople evolved from simple response suggestions to fully autonomous customer service agents, the architecture that powers their conversational workflow builder, and how they designed eval loops that include their _customers_ as part of the quality process. You’ll learn how the team: - Moved from “LLMs will solve everything” to finding the right balance between code, agents, and guardrails - Designed evals that run in production to detect hallucinations before an email ever reaches a customer - Helped non-technical users build automations conversationally — and taught them decomposition along the way - Turned customers’ feedback loops into eval pipelines that improve product quality over time It’s a fascinating look at how one startup is rethinking what it means to “work with AI” — not as a tool, but as a teammate.

What does it really take to build an AI agent inside an AI platform—especially when you’re using that same platform to build the agent? In this episode of Just Now Possible, Teresa Torres talks with SallyAnn DeLucia (Director of Product at Arize) and Jack Zhou (Staff Engineer at Arize) about the journey of building Alyx, their AI agent designed to help teams debug, optimize, and evaluate AI applications. They share the scrappy beginnings—Jupyter notebooks, hacked-together web apps, and weekly dogfooding sessions with their customer success team—and the hard-earned lessons about evals, tool design, and how to prioritize early skills. Along the way, you’ll hear how cross-functional experience, intuition-building, and customer insight shaped Alyx into a product that’s now central to the Arize platform. If you’ve ever wondered how to move from vibe checks and one-off prototypes to systematic improvement in your AI product, this episode is for you.

How do you know if your AI product is actually any good? Hamel Husain has been answering that question for over 25 years. As a former machine learning engineer and data scientist at Airbnb and GitHub (where he worked on research that paved the way for GitHub Copilot), Hamel has spent his career helping teams debug, measure, and systematically improve complex systems. In this episode, Hamel joins Teresa Torres to break down the craft of error analysis and evaluation for AI products. Together, they trace his journey from forecasting guest lifetime value at Airbnb to consulting with startups like Nurture Boss, an AI-native assistant for apartment complexes. Along the way, they dive into: - Why debugging AI starts with thinking like a scientist - How data leakage undermines models (and how to spot it) - Using synthetic data to stress-test failure modes - When to rely on code-based assertions vs. LLM-as-judge evals - Why your CI/CD set should always include broken cases - How to prioritize failure modes without drowning in them Whether you’re a product manager, engineer, or designer, this conversation offers practical, grounded strategies for making your AI features more reliable—and for staying sane while you do it.

How do you build an AI-powered assistant that teachers will actually use? In this episode of Just Now Possible, Teresa Torres talks with Thom van der Doef (Principal Product Designer), Mary Gurley (Director of Learning Design & Product Manager), and Ray Lyons (VP of Product & Engineering) from eSpark. Together, they’ve spent more than a decade building adaptive learning tools for K–5 classrooms—and recently launched an AI-powered Teacher Assistant that helps educators align eSpark’s supplemental lessons with district-mandated core curricula. We dig into the real story behind this feature: - How post-COVID shifts in education created new pressures for teachers and administrators - Why their first instinct—a chatbot interface—failed in testing, and what design finally worked - The technical challenges of building their first RAG system and learning to wrangle embeddings - How their background in education shaped a surprisingly rigorous eval process, long before “evals” became a buzzword - What they’ve learned from thousands of teachers using the product this school year It’s a detailed look at the messy, iterative process of building AI-powered products in the real world—straight from the team doing the work.

When ChatGPT launched, Stack Overflow faced a cataclysmic shift: developer behavior was changing overnight. In this episode, Teresa Torres talks with Ellen Brandenburger, former product leader at Stack Overflow, about how her team navigated the disruption, prototyped AI features, and eventually built an entirely new business line. Ellen shares the inside story of Overflow AI—from the first scrappy prototypes of conversational search, through multiple iterations with semantic search and RAG, to the tough decision to roll the product back when it couldn’t meet developer standards. She also explains how Stack Overflow turned a looming threat into opportunity by creating technical benchmarks and licensing its Q&A corpus to AI labs. This episode offers a rare look at what it really takes to adapt when a platform-defining shift hits—and what product managers, designers, and engineers can learn about prototyping, evaluating quality, and building in uncertainty.

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. The first full episode drops on Thursday, September 18th. Don't miss it!