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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.
Show Notes
Guests
- Mark Barbir – CEO, Earmark
- Sanden Gocka – Co-Founder, Earmark
What we cover in this episode:
- How Earmark differs from generic AI notetakers by producing finished work, not just summaries
- The pivot from Apple Vision Pro presentation coaching to a web-based meeting assistant
- Running multiple agents in parallel during live meetings
- Template-based agents: Engineering Translator, Make Me Look Smart, Acronym Explainer
- Personas that simulate absent team members (security architect, legal, accessibility)
- Why ephemeral mode (no data storage) became a selling point for enterprise
- Reducing AI costs from $70/meeting to under $1 through prompt caching
- Why GPT 4.1 still beats newer models for prose quality in their use case
- The limits of vector search for analysis questions across meetings
- Building agentic search with multiple retrieval tools (RAG, BM25, metadata queries, bespoke summaries)
- Designing for product managers as the extreme user to solve for everyone
- Their vision for an AI chief of staff that goes beyond automating deliverables
Resources & Links
- Earmark — Productivity suite where the work completes itself
- ProductPlan — Roadmapping tool where both founders previously worked
- Granola — AI notetaker mentioned for comparison
- Assembly AI — Speech-to-text service used by Earmark
- OpenAI API — LLM provider with prompt caching support
- Cursor — AI code editor with build integration in Earmark
- V0 by Vercel — AI prototyping tool with build integration in Earmark
Chapters
00:00 Introduction to Earmark Founders
00:28 Background and Experience
01:05 What Does Earmark Do?
01:23 AI and Productivity
03:09 Comparing Earmark to Competitors
03:41 Earmark’s Unique Features
05:53 Templates and Personas
10:06 Technical Details and Development
17:12 Early Product Versions and Challenges
28:44 Understanding Prompt Caching
29:49 Managing Multiple Tools and Costs
30:59 Optimizing Transcript Summarization
35:11 Challenges with Context and Reasoning Models
38:10 Innovative Search and Retrieval Techniques
44:06 Creating Actionable Artifacts from Meetings
48:30 Ensuring Quality and Managing Hallucinations
58:20 Future Vision for AI Chief of Staff
Full Transcript
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