AI Meeting Minutes & Action Items Extractor

    Inspiration: After remote work popularization, Zoom/Teams meeting recordings pile up. Organizing meeting minutes and follow-up action items from hours of recordings is extremely time-consuming and tedious work, a typical efficiency bottleneck within companies.

    Target Customers: Project managers, team leaders, administrative assistants, any professionals who frequently attend meetings.

    Pain Points:

    • Time-consuming: Need to re-listen to entire meeting recordings to organize notes.
    • Information Omission: Easy to miss key decisions or specific tasks assigned to someone.
    • Follow-up Difficulties: Action items scattered throughout conversations, hard to track.

    Solution (Micro-SaaS): An intelligent meeting assistant. Users upload meeting recordings or video files, AI automatically completes transcription and intelligently extracts meeting summaries, key decision points, and clear action item lists (including responsible persons and deadlines).

    MVP Core Features:

    • Audio/Video Upload: Support common MP3, MP4, M4A formats.
    • High-Precision Transcription: Provide transcripts with speaker identification.
    • AI Summary Generation: Generate concise meeting content summaries.
    • Action Item Extraction (Core): Automatically identify "who needs to do what by when" action items and list them.
    • Decision Point Highlighting: Mark key consensus or decisions reached in meetings.
    • Calendar/Task Tool Integration: (Advanced feature) One-click send action items to Trello, Asana, or Google Calendar.
    • Email Sending: Send meeting minutes and action items via email to all attendees.

    Development Investment (Technical Implementation): Medium. Similar to podcast summary tools but requires higher understanding of business scenarios.

    • LLM API Calls:
      • Speech Transcription: OpenAI Whisper API or AssemblyAI, preferably supporting speaker diarization.
      • Minutes Extraction: GPT-4 Turbo or Claude 3 Opus. Prompt is key - need to train model to identify specific language patterns of task assignment in business conversations (e.g., "So, John will take the lead on the report, due next Friday.").
    • Hugging Face Open Source Models:
      • Transcription: openai/whisper-large-v3 combined with speaker diarization libraries like pyannote.audio.
      • Minutes Extraction: meta-llama/Llama-3-70B-Instruct needs careful prompt design and few-shot examples to accurately distinguish regular discussions from actionable follow-up items.