Actionify – AI-Powered Meeting Analysis

Inspiration

I built Actionify to solve a simple but common problem: important insights and action items often get lost after meetings. Long recordings and scattered notes make follow-ups inefficient, so I wanted an AI tool that turns meetings into clear, actionable results.

What it does

Actionify analyzes meeting recordings or text and extracts what matters:

• Transcribes audio/video with speaker identification
• Generates summaries, action items, and decisions
• Performs sentiment analysis across speakers
• Displays insights in a clean, interactive dashboard
• Exports reports as PDF, Markdown, Text, or JSON

How I built it

I used a vibecoding approach focused on shipping fast and building something real.

Tech stack:
React + TypeScript, Vite, Tailwind CSS, Recharts, jsPDF
AssemblyAI (transcription), OpenRouter (LLM analysis), Cloudinary (media storage)

Challenges

• Coordinating multiple AI APIs
• Managing long-running AI tasks in the UI
• Prompt engineering for consistent structured output
• Exporting dynamic content cleanly

What I learned

• Advanced React and TypeScript patterns
  
• API integration and async workflows
  
• LLM prompt engineering
  
• Performance optimization for large files

What’s next

Real-time analysis, meeting platform integrations, team collaboration features, and multilingual support.

Built With

• jspdf
• react
• reactrouterdom
• rechart
• tailwindcss
• typescript
• vite