AI Meeting Assistant
Glossary

Tasks and follow-ups automatically extracted from meeting discussions by AI analysis. Most meeting assistants identify who is responsible and what needs to be done, saving teams from manually reviewing recordings or notes after the fact.

Software that uses artificial intelligence to record, transcribe, summarize, and analyze meetings. These tools range from cloud-based services that join calls via bots to on-device apps that process audio locally. See our full rankings for a comparison of the leading options.

A recording method where a virtual participant (bot) joins your video call to capture audio and video. The bot appears in the attendee list and is visible to all participants. Tools like Otter.ai and Fireflies.ai use this approach.

Audio transcription and AI analysis performed on remote servers. Meeting audio is uploaded over the internet, processed by the provider's infrastructure, and results are returned to the user. This is the most common approach, but raises privacy considerations since your audio leaves your device.

AI-powered analysis that goes beyond transcription to extract insights like talk ratios, sentiment, coaching opportunities, and deal signals from meetings. This category is common in sales-focused tools and is a core strength of platforms like Fireflies.ai and Avoma.

Adherence to the European Union's General Data Protection Regulation, which governs how personal data (including meeting recordings) is collected, stored, and processed. For meeting assistants, GDPR compliance typically requires explicit consent from participants before recording, clear data retention policies, and the ability for users to request data deletion.

A system that connects insights across multiple meetings, building a searchable network of topics, decisions, and relationships over time. Rather than treating each meeting in isolation, a knowledge graph lets you trace how a project or topic has evolved across weeks of conversations.

See Bot-Based Recording. A software agent that joins video conferences as a participant to record the session. The bot is visible to all attendees and typically requires calendar integration or a meeting link to join.

A recording method where the app captures system audio directly from your device, without joining the call as a separate participant. No bot appears in the meeting. Tools like Hedy use this approach for more discreet, privacy-friendly recording. Also called system-level audio capture.

AI-powered removal of background sounds (typing, dogs, construction) from audio during calls or recordings. Some tools process noise cancellation on-device in real time, while others apply it during post-meeting transcription in the cloud.

Audio transcription and AI analysis performed entirely on the user's computer or phone, without sending data to external servers. This provides stronger privacy since audio never leaves the device. Hedy and MacWhisper are examples of tools that offer on-device transcription.

Converting speech to text as it happens during a live meeting, rather than processing a recording after the meeting ends. Real-time transcription lets participants follow along with a live text feed and is useful for accessibility, note-taking, and quick reference during calls.

The process of identifying and labeling which person said what in a multi-speaker recording. Accuracy varies significantly across products and depends on factors like the number of speakers, audio quality, and whether the tool has voice samples to learn from.

See Native Audio Capture. A method of recording that captures all audio playing through the device's audio system, without adding a bot or virtual participant to the meeting.

How correctly a tool converts spoken words to written text, typically measured as Word Error Rate (WER). Factors affecting accuracy include audio quality, accents, background noise, and specialized vocabulary. See our methodology for how we test transcription quality.

An open-source speech recognition model developed by OpenAI. Several on-device meeting assistants, including Hedy and MacWhisper, run Whisper locally for private transcription. The model is available in several sizes that trade off speed for accuracy.

The standard metric for measuring transcription accuracy. Calculated as the number of errors (insertions, deletions, substitutions) divided by the total number of words. Lower is better; professional human transcription typically achieves 4-5% WER.