Multi-Speaker Meeting Transcriber

Teams struggle to transcribe meetings with multiple speakers accurately, often getting who said what wrong. This tool automatically identifies speakers, timestamps, and transcribes conversations in real-time. Perfect for remote teams, podcast producers, and researchers who need accurate speaker-diarized transcripts.

Speaker diarization has gotten dramatically more accurate since OpenAI's Whisper and pyannote.audio matured, which means the underlying tech stack is now accessible to a solo developer without needing to train custom models. Otter.ai is the obvious incumbent and already owns significant mindshare in this space, with Fireflies and Zoom's native transcription also eating at the addressable market. The $1k–$5k/mo revenue band is plausible for a niche-focused wedge — say, qualitative researchers or podcast editors — but requires keeping churn low since the use case is often project-based rather than habitual daily usage. The biggest risk is commoditization: Zoom, Teams, and Google Meet are all shipping native diarization features, which steadily shrinks the gap a standalone tool can exploit.