AI meeting Assistant

• 

  ui

• 

  summary output

• 

  excel downloads

In today's fast-paced remote and hybrid work environments, meeting overload has become a real challenge. I often found myself struggling to recall key discussion points, assign tasks, or follow up effectively after meetings. This inspired me to build an AI Meeting Assistant that could automate note-taking, summarize conversations, and highlight action items — all without disrupting the natural flow of discussion.

🛠️ How I Built It I used a layered architecture consisting of the following components:

Speech-to-Text Layer Leveraged Whisper API to convert real-time audio into accurate text transcripts.

NLP & Summarization Layer Applied large language models (LLMs) via OpenAI’s GPT-4 API to generate concise summaries and extract:

Key points

Action items

Decisions made

Front-End Interface Developed using React.js and Tailwind CSS to ensure a clean, responsive UI for users to view, export, and interact with notes.

Export & Sharing Enabled CSV/XLSX export of meeting summaries for integration with task managers or documentation workflows.

I also considered token limitations and prompt design techniques to optimize LLM responses and keep outputs structured.

💡 What I Learned How to fine-tune LLM prompts to improve output consistency

Techniques for efficient real-time transcription using Whisper

Importance of user-friendly UI in adoption of productivity tools

Trade-offs between latency, accuracy, and summarization granularity

I also explored how memory and context length affect multi-speaker summarization.

⚙️ Challenges I Faced Real-time Processing: Ensuring Whisper and LLM responses could keep up with live conversations without lag

Speaker Attribution: Differentiating between multiple speakers in transcripts

Token Management: Managing context size and input length for long meetings while retaining coherence

Data Privacy: Handling sensitive meeting data responsibly and securely

🧠 Bonus — A Peek into My Thought Process I aimed to minimize information loss by using summarization functions that approximate:

Summary

argmax = [Relevance(S,T)−λ⋅Length(S)] where 𝑇 T is the transcript, and 𝜆 λ is the trade-off parameter for verbosity.

Built With

• github
• groq
• javascript
• openai
• promptengineering
• react
• tailwind