AI - ASR / LLM: in depth meetings analysis
Revolutionizing meeting management with AI: Sweez’s enhanced meeting analysis platform
1. Business objectives
The client is building a platform designed to optimize how businesses handle meetings by removing the burden of note-taking. The tool aggregates all company meetings and enables employees to easily retrieve relevant information without wasting time transcribing or summarizing. Sweez’s main goal is to create a seamless experience where meeting data is readily available, searchable, and well organized. The company sought to enhance this platform with AI technologies such as Automatic Speech Recognition (ASR) and Large Language Models (LLM) to improve the accuracy and usefulness of meeting summaries, enrich knowledge extraction, and automate the creation of actionable insights.
2. The AI / Data challenges faced
The core challenge faced by Sweez was the inefficiency and inaccuracies associated with manual meeting transcription and the difficulty in summarizing long, complex conversations. The process of reviewing and extracting relevant information from meetings was time-consuming and prone to errors, especially when dealing with multiple participants and diverse discussion topics. Additionally, the existing system lacked the ability to segment meetings into clear chapters or to automatically generate summaries that captured key points and action items.
Sweez needed an AI solution capable of transcribing conversations accurately, identifying who said what (diarization), and automatically breaking down meetings into easily digestible segments. Furthermore, the platform required a robust Q&A extraction mechanism to provide metadata on meeting topics and participants, which was essential for making meetings actionable and improving follow-up efficiency.
3. How Bubo SAS helped
Bubo team worked to implement a comprehensive AI solution using state-of-the-art technologies tailored to address the specific needs of meeting analysis.
- ASR & diarization: Bubo SAS developed an automatic transcription system that converted meeting audio into text with high accuracy. The integration of speaker diarization allowed the system to identify and tag each speaker’s contributions based on initial samples, ensuring clarity and context in the final transcripts.
- Meeting segmentation: To enhance readability and usability, Bubo team implemented a segmentation model that divided the meeting transcript into chapters, based on natural discussion transitions. This feature allowed users to navigate large meetings more efficiently, focusing on the most relevant sections without needing to review the entire conversation.
- LLM-based summarization & metadata extraction: Bubo SAS utilized large language models (LLMs) to generate high-quality automatic summaries, which captured the essence of each meeting. Additionally, an entity-based summarization approach was developed to provide customized summaries that organized information by key individuals or topics. Q&A extraction was incorporated, enabling the system to pull relevant meeting data for quick reference. This made meeting outcomes actionable, enabling better follow-up and decision-making.
- AI integration into the platform: The AI features were seamlessly integrated into the Sweez platform, ensuring that all meetings were automatically processed and structured in a way that was easy to navigate.
4. Results
The AI-powered enhancements delivered substantial improvements to the platform and its users:
- Diverse data types: within this project we worked not only on text data as we usually do, but also on audio and visual data, multi modality is the key to go at the next level in this area.
- Automatic transcription, segmentation, and summarization significantly cut down the time required for employees to review meetings and extract key insights.
- Improved accuracy: The diarization and speaker identification features led to a 80%+ accuracy rate in capturing and attributing discussions correctly.
- Actionable insights and dashboards: Entity-based summaries and Q&A extraction enabled users to quickly access key points related to specific individuals or topics, improving follow-up efficiency.
These improvements resulted in more efficient meeting management, better collaboration, and increased productivity across teams.