Make meetings more efficient and traceable
In today’s digital age, scheduling online meetings is easier than ever. Those having multiple meetings in a day might relate – keeping track of conversations, information exchanged, and decisions made can be overwhelming. An automated way of documenting the information could help making meetings more efficient and traceable. The idea for MeetingMind was born.
01. Intro
02. Challenge
03. Solution
04. Results
01. INTRO
Make meetings more efficient and traceable
In today’s digital age, scheduling online meetings is easier than ever. Those having multiple meetings in a day might relate – keeping track of conversations, information exchanged, and decisions made can be overwhelming. An automated way of documenting the information could help making meetings more efficient and traceable. The idea for MeetingMind was born.
02. CHALLENGE
Translating Social Impact into Actionable Insights
The idea was simple and clear. Why not use AI to automatically create meeting notes and use them to create added value? Reduce monotone work? In reality there were some challenges to overcome:
Where to get the audio meeting from?
How to transform the audio into text?
How to post-process textual information?
How to make all the above usable for potential users in a convenient way?
03. SOLUTION
Leverage deep learning
The technical solution for MeetingMind consists of leveraging the capabilities of three different neural networks:
faster-whipser for the transcription task
pyannote.audio for speaker diarization
META llama3 70B for text based tasks
Due to data-privacy reasons, the first two neural networks are self-hosted. For testing purposes we opted for the META llama3 70B model hosted by www.groq.com. This model could be substituted by a LLM of choice, either self-hosted or by using cloud providers such as openAI or Anthropic.
The only required input is an audio file which contains the audio of the meeting recording.
04. RESULT
Fully integrated in the meeting environment
The result is an app integrated in Microsoft Teams (the main communication tool in our company). It can be added to an online meeting. The meeting is recorded using the Teams built-in recording function. Once the recording is available for download, the app notifies the meeting attendees and the audio transcription is stared upon user input. The output format of the meeting notes can either be chosen via a set of predefined template depending on the meeting setting, for example “weekly meeting”, or individually specified by user input.
CONCLUSION
Increasing efficiency and traceability of online meetings
At Cactus we’re committed to finding ways to apply current AI systems on everyday tasks with beneficial outcome. MeetingMind helps you get more out of your meetings, whether it’s a team discussion, a client call, or a project update, making every conversation more productive and meaningful.
