A finished research run on an example topic, shown in full — the findings, the sources behind them and the product versions are all the run's own output, except that a product name may have been changed where it matched an existing brand.

Back to the run overview AI meeting notes

Trendy Quick win

Sidecar

A bot-free background capture app covering video calls, in-person conversations, and phone calls in a single local pipeline, replacing the need to juggle separate capture modes for each context.

01

What to build

  • MVP

    A macOS menu-bar app that captures system/mic audio regardless of source (Zoom, in-person, phone via AirPlay/Bluetooth routing), auto-tags the context, and runs local transcription and diarization on all of it in one unified log.

  • Why it can be better

    Otter splits bot, bot-free, and mobile into separate modes, and Hyprnote is scoped to in-person only; no tool spans all capture contexts in one continuous local history as this does.

  • Revenue model

    Monthly subscription for the desktop+mobile bundle, paid by frequent business travelers and salespeople who capture across many contexts.

  • Target user

    Field sales reps, recruiters, and traveling executives who take meetings across video, phone, and in-person settings.

02

Context

The opportunity this answers

Build a local-first meeting-memory layer for professionals that unifies transcripts from any notetaker or capture mode into one private, searchable history.

Why now

A wave of open-source local-first notetakers built on Rust/Swift/Tauri has already proven demand for on-device processing, with diarization and Whisper models now standard, high-download building blocks anyone can bundle, while search interest for generic 'AI meeting notes' has plateaued — signaling the crowded cloud tier is saturated even as privacy-driven local alternatives keep multiplying, and none of them yet unify capture contexts, ingest rival platforms' exports, or sync back out privately.