Just Now Possible

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.

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.

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.

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.