I care about meetings only insofar as they create momentum and outcomes. What if your meetings could actually produce the artifacts you need—specs, tickets, slides—before the call even ends?
I recently listened to an episode of Just Now Possible where 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. As a product leader, this premise hits the sweet spot of agentic AI, real-time AI workflows, and ruthless focus on outcomes over output.
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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. That’s the bar I want from AI in the room: finished work, not notes.
What impressed me most was the clarity of their pivot. They moved from an Apple Vision Pro presentation coaching tool to a web-based meeting assistant. I’ve made similar calls: when the distribution path and daily workflow are obvious, you follow the user’s gravity. This shift unlocked a broader surface area—PMs, engineers, design partners—and made agentic workflows useful where work actually happens.
They also turned a technical constraint into a commercial advantage. Their ephemeral (no-storage) architecture became a feature for enterprise sales. I’ve seen this repeatedly in AI risk management: privacy-by-design and clear data governance reduce friction with security reviewers and accelerate procurement. For many enterprises, “we don’t store your data” is the win condition.
Cost discipline was another standout. They tackled the hard problem of making real-time AI affordable—from $70 per meeting down to under a dollar through prompt caching. That’s not just optimization; it’s product strategy. Choices like model selection, context window management, and retrieval-first pipeline design determine whether a feature can scale to every meeting or remains a demo.
On capability design, the team leaned into templates and simulated stakeholders to ship value fast. Template-based agents: Engineering Translator, Make Me Look Smart, Acronym Explainer. Personas that simulate absent team members (security architect, legal, accessibility). This is exactly how I frame early AI workflows: remove friction for the product trio, anticipate blockers, and let the agent do the tedious, error-prone first pass.
They were refreshingly pragmatic about models. Why GPT 4.1 still beats newer models for prose quality in their use case is a reminder that “best” is contextual. When the job-to-be-done is precise prose and production-grade artifacts, consistent quality trumps leaderboard buzz. Of course, they also invest in guardrails to ensure quality and manage hallucinations—another non-negotiable for enterprise adoption.
Search and analysis across time is where many AI products stumble. They explained the limits of vector search for analysis questions across meetings and how they’re building agentic search with multiple retrieval tools (RAG, BM25, metadata queries, bespoke summaries). I couldn’t agree more: analysis requires reasoning over structure, time, and purpose—not just semantic proximity. Layered retrieval with stateful agents beats a single embedding call.
They also articulated a crisp user thesis: design for product managers as the extreme user to solve for everyone. In my experience, if you satisfy the PM’s bar for clarity, traceability, and actionability, engineers, designers, and go-to-market teams benefit immediately. That’s how you earn daily active use, not once-a-week novelty.
For builders curious about the stack and comparables, they discuss services and tools like Assembly AI for speech-to-text, OpenAI API with prompt caching support, and build integrations with Cursor and V0 by Vercel. They also reference Granola as a comparison point and nod to ProductPlan, where both founders previously worked. If you want to try the product, here’s Earmark—a productivity suite where the work completes itself.
If 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. Personally, I see this as a credible path toward an AI chief of staff—their vision goes beyond automating deliverables to orchestrating judgment, compliance signals, and cross-functional readiness.
The episode covers the founder backstory, what Earmark does, comparisons to competitors, unique features, templates and personas, technical decisions, early versions and challenges, optimizing transcript summarization, managing multiple tools and costs, challenges with context and reasoning models, innovative search and retrieval techniques, creating actionable artifacts from meetings, ensuring quality and managing hallucinations, and the future vision for an AI chief of staff. It’s a full-spectrum look at building with agentic AI, not just talking about it.
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Inspired by this post on Product Talk.
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