Just Now Possible

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.

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.