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Quill, developed by the Quill Team and currently available at version 2.6.7 across three released versions, is an AI meeting assistant designed to help users stay organized privately and accurately throughout their workday. Falling into the productivity and meeting-management software category, it records, transcribes, and summarizes meetings while storing audio and transcripts on the user's own device by default, with no uploads or server-side recording. This local-first approach makes meeting data permanently accessible, searchable, and fully owned by the user, enabling engagement in conversations without worrying about dropped details. Quill differentiates itself through three core strengths: trust, accuracy, and customization. On the trust side, users choose which AI processes their notes—local models, their own API key, private inference servers, or Quill's cloud—and sharing is strictly opt-in, per-meeting, and end-to-end encrypted. Accuracy features include high-quality local transcription that holds up across accents and meeting formats, speaker recognition that learns participants' voices over time, multilingual support for mixed-language conversations, the ability to learn custom industry terms, names, and acronyms from note replacements, and search across every meeting ever recorded. Customization and integration capabilities include templates tailored to specific meeting types such as one-on-ones, client sessions, sales calls, and board meetings, automatic template application based on meeting type, and structured outputs that connect with tools users already rely on. Quill is designed for professionals who want an automated notetaking solution without bots joining their calls or their data leaving their machines. Getting started involves installing Quill on a Mac or Windows computer, after which the software detects upcoming meetings and begins capturing them. Common use cases include documenting recurring team meetings, preserving client conversations, generating summaries for follow-up, and maintaining a long-term searchable archive of recorded discussions for reference years later.
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