AI Meeting Transcription and Summaries: Tools and Setup
Practice3 minInnovialabs editorial team
In short
Record the meeting, transcribe it with a speech-to-text model, then have an LLM extract decisions, tasks and owners using a fixed template — and always tell participants they are being recorded.
How it works in short
An AI meeting summary is a chain of three steps:
- Recording — audio from a call or a meeting room.
- Transcription (speech-to-text) — a model turns speech into text, often with speaker labels (diarization).
- Summary — an LLM reads the transcript and, following a template, outputs decisions, tasks, deadlines and open questions.
Result quality usually depends on the first step: no model can fix bad audio.
Three approaches
| Approach | Pros | Cons |
|---|---|---|
| Built-in video call features (Zoom, Google Meet, Teams) | Nothing to set up | Tied to the platform, limited control over the format |
| Standalone assistant services that "join" the call | Searchable meeting history, CRM and task tracker integrations | Data goes to a third party, a bot in the call can make people uneasy |
| Your own pipeline: STT model + LLM via API or on-premise | Full control over data and summary format | Requires development and maintenance |
How to choose: for internal, low-stakes meetings, start with built-in features. If integrations and search matter, look at assistant services. If you discuss trade secrets or personal data, or need a specific language (such as Uzbek), build your own pipeline and test it on your real recordings.
Getting useful minutes, not a retelling
The main mistake is asking for "a short summary". You get a retelling nobody reads. Give the model a strict structure:
You are writing meeting minutes from a transcript.
Output exactly these sections:
1. Decisions — what was finally agreed.
2. Tasks — a table: task | owner | deadline.
3. Open questions — what was not resolved and who owns it.
Do not add anything that is not in the transcript.
If an owner or deadline is not mentioned, write "not specified".
What else helps:
- A participant list and glossary at the top of the request — the model confuses names and terms less often.
- Long meetings: split into parts, summarize each, then merge.
- A human reviews the minutes before sending — at least the meeting host.
Audio setup
- Use an external microphone or a speakerphone in the room, not a laptop in the corner.
- Ask people to speak one at a time: crosstalk breaks speaker labels.
- In calls, record separate tracks per participant if the platform allows it.
- Check how the model handles mixed speech (for example Russian, Uzbek and English terms) — a common weak spot.
Consent and confidentiality
- Announce the recording at the start and in the invitation. Consent requirements depend on local law — check with a lawyer.
- Find out where data is stored and whether it is used to train models. This is usually stated in the terms of service or business plan settings.
- Limit access: transcripts of client or HR meetings should not sit in a shared folder.
- Set a retention period: audio can often be deleted once the minutes are checked.
- For sensitive meetings, consider local models running on your own servers.
Common mistakes
- Trusting the minutes without review: an LLM may "invent" an owner or a deadline.
- Not fixing the format — and getting a different structure every time.
- Recording everyone always without explaining why — it erodes team trust.
- Not connecting minutes to the task tracker: tasks stay buried in a document.