Just Now Possible

Building Earmark: How a Two-Person Team Turned Meetings into Finished Work

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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

        ...more
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        Just Now PossibleBy Teresa Torres