Building Earmark: Real-time voice AI, privacy by design, and founder lessons

2026-02-09 · Source: AssemblyAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Entrepreneurship & Start-ups · Depth: Intermediate, extended

Summary

Earmark is a productivity suite designed to transform real-time meeting conversations into structured work artifacts such as documents, tickets, updates, and next steps. The product evolved significantly from its initial concept as an AR/VR speech coaching tool for Vision Pro, pivoting after user research revealed a lack of preparation for presentations. Earmark now focuses on automating the creation of deliverables during meetings, aiming to reduce cycle times for R&D teams and enable product managers to keep pace with engineers. The company uses AssemblyAI for its transcription needs, citing its speed, accuracy, and crucial unlimited concurrency streams as key advantages over previous providers, which struggled with scalability and complex plumbing requirements. Earmark's internal use cases include brainstorming and ideation, converting unstructured discussions into structured outputs like requirements or go-to-market messaging, and generating prototypical flows directly from conversations.

Key takeaway

For Product Managers and R&D leaders seeking to accelerate development cycles and reduce post-meeting overhead, Earmark offers a compelling solution. You should explore how real-time voice AI platforms can convert unstructured conversations into immediate, actionable work items, thereby freeing up capacity for strategic tasks. Consider piloting such tools to streamline artifact creation and proactively address team blockers, potentially integrating with existing project management systems like Linear or code editors like Cursor.

Key insights

Earmark transforms real-time meeting conversations into actionable work artifacts, enhancing productivity and reducing manual follow-up.

Principles

• Privacy by design is paramount for voice AI products.
• UX for voice AI must be forgiving and low-friction.
• Challenge industry dogma; chart your own path.

Method

Earmark listens to meetings, transcribes speech, and uses AI to generate structured work artifacts (e.g., engineering specs, PRDs) and allows real-time modification via "vibe docking" for immediate output.

In practice

• Use real-time transcription to auto-generate meeting deliverables.
• Integrate AI-generated tasks directly into project management tools.
• Implement a "temporary mode" for sensitive voice data.

Topics

• Voice AI
• Meeting Automation
• AI Productivity Tools
• Real-time Transcription
• Product Development Strategy

Best for: Product Manager, Software Engineer, Entrepreneur

Related on AIssential

• AI note-taking tools for 2026: where each one fits — Effective AI note-taking requires a layered approach, combining capture, transcription, and synthesis tools for diverse meeting environments.
• Even your voice is a data problem — End-to-end deep learning and optimized model architectures are crucial for scalable, accurate, and affordable voice AI.
• AI Will Rewrite Employee Experience, And Deep Listening Shows How — AI-driven deep listening offers continuous, emotion-aware insights into employee experience from digital interactions.
• 😺 Watch LIVE NOW: Building AI Voice Agents w/ LiveKit's Ben Cherry — Building effective real-time AI voice agents demands addressing latency, interruptions, and audio quality for production viability.
• Privacy by design: Building Earmark — Voice AI products require privacy by design and a forgiving, low-friction user experience.
• Talat’s AI meeting notes stay on your machine, not in the cloud — Local-first AI notetaking offers enhanced privacy and user control over data compared to cloud-based alternatives.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AssemblyAI.