AI meeting management: reducing friction with 6 smart features.

Transcripts often get cluttered with “ums,” “you knows,” and redundant phrases. While they seem small, these disfluencies make reading notes frustrating and reduce clarity.

We designed AI-powered speech optimization to clean transcripts automatically, producing more professional notes without altering the speaker’s intent:

• inline detection: filler words surfaced visually in the transcript, so users immediately see what’s being cleaned;
• smart removal: one-click “Remove” button applies batch clean-up while keeping the speaker’s intent intact.

In cross-functional meetings, jargon and technical terms can create barriers. What’s obvious to an engineer may be confusing for a marketer or client. Raw transcripts often amplify this problem: participants read back dense phrases without any context.

Our solution: terminology support built into the transcript UI. Users can highlight any word or phrase, then choose Explain this. The AI instantly generates a plain-language definition in a side panel:

• inline interaction: terminology appears in context, with a simple right-click menu (Copy, Translate, Explain this);
• AI explanations: technical terms unpacked into human-friendly definitions;
• actionable outputs: users can save explanations as notes or add follow-ups.

Background distractions are invisible but constant: chatter, typing, echoes. While AI can remove these sounds in real time, users often don’t realize what’s happening in the background or whether the feature is active:

• granular control: users can enable the type of suppression they need (voices, general noise, echo);
• visual feedback: checkboxes and confirmation pop-ups show when filters are active.

Traditional meeting notes are either incomplete or overwhelming with long transcripts no one reads or bullet points scattered across apps. Teams needed a way to turn conversations into structured outcomes without the manual effort.

This note generation works around flexible templates and workflows. At the start of a meeting, users can select a template that fits the context: Customer discovery, Status update, Job interview, and more. During the session, the system listens and produces clear notes with summaries and next steps:

• template-driven capture: context-specific note styles (e.g. discovery vs. interview) ensure outputs are relevant;
• task integration: notes convert directly into to-dos, complete with assignees and priorities;
• editable by design: users can add context, keeping the AI as assistant, not replacement.

Finding a single decision or tracking a project detail often means scrolling through endless transcripts or manually configuring complex filters. This makes meeting insights inaccessible to many team members, especially those less technical.

Now, instead of constructing advanced filters, participants can simply type a natural request (e.g., “All issues from Web App that are in progress, dev team has to finish them before 23 May.”), and the system translates this into a structured query.

Between different time zones and external participants, scheduling often takes longer than the meeting itself.

We added AI-assisted scheduling to the meeting workspace. After notes and tasks are generated, users can ask the system to propose a follow-up meeting:

• cross-calendar intelligence: AI checks availability and proposes the best-fit time;
• external integration: scheduling links support coordination with people outside the org.
• end-to-end workflow: from meeting notes → follow-up scheduling → calendar invites → video link, all in one flow.