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Extracting User Insights – AI-Powered Feedback Analysis
Developed an AI-powered system that analyzes large volumes of user feedback to extract meaningful insights and trends. The platform processes textual data such as customer reviews, survey responses, and user comments using Natural Language Processing (NLP) techniques.
The system performs text preprocessing, sentiment analysis, and topic detection to identify common patterns, user pain points, and feature requests. The extracted insights are then presented through a structured visualization layer, enabling businesses to better understand customer needs and make data-driven product decisions.
Technologies Used: Python, NLP, Data Processing, Backend Development
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Ticketify — Event Ticketing Platform (MERN)
Ticketify is a full-stack ticketing platform where organizers can create events, sell tickets and track sales, while users can discover events and purchase tickets.
The backend focuses on real-world production processes.
Key implementations:
• JWT authentication with access & refresh tokens
• Google OAuth login
• Event-driven email system (account verification, login alerts, purchase confirmations, password recovery)
• Organizer dashboard with sales KPIs
• QR ticket validation system
• Clean MVP payment workflow
Tech Stack:
Node.js • Express • MongoDB • React • JWT • OAuth • Resend
great work!
AI-Powered Outreach Automation (n8n)
Built a workflow that turns outreach into a structured pipeline.
The system:
• Pulls verified leads from Supabase
• Uses AI to generate personalized emails
• Sends outreach via Gmail
• Monitors replies and analyzes sentiment
• Routes responses automatically (book meeting, review, or archive)
• Sends follow-ups if there’s no reply after 48 hours
Result: a fully automated pipeline that moves leads → conversations → booked meetings.
Designed for creators, agencies, and teams running outbound outreach at scale.
Nice workflow. Using AI to personalize outreach and track replies in one pipeline is really useful.
Built a full-stack AI automation system that transforms how the company responds to RFPs.
Users upload RFP documents (PDF/Excel) through a web portal. An n8n pipeline processes the upload: AI parses the document to extract key fields (issuer, scope, budget, deadline), classifies the service line, matches content from an Airtable library (team bios, references, portfolio), and generates 80-90% complete proposal drafts with cover letter, company overview, technical approach, and more. Output is a formatted Word document ready for review.
Results:
✅ Reduced proposal creation from 4-6 hours to under 30 minutes
✅ 90% auto-complete rate on proposal sections
✅ 14+ RFPs processed through the pipeline
✅ Full proposal dashboard with status tracking
Tech: n8n, OpenAI, Airtable, Vercel, Webhooks
This is a really interesting automation workflow, Peter. Using AI to parse RFPs and streamline proposals is a smart use case.
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