Trip It!
Web
11.04.2026
Product Designer, Frontend Designer & Implementor
- Tensorix
- n8n
- Lovable
- GoogleADK
- Leaflet
- React
Built during the "Give(a) go" workshop organized on 11.04.2026, sponsored by Tensorix, n8n, and Lovable.
🧭 My Role
We were a team of six. I took the lead on product decisions: after our brainstorming I shaped the interface around what the backend would deliver, made the product calls that guided our designers, divided the tasks across the team, and got the app ready for the final presentation, which we then pitched together.
💡 The Ideation: Why did we build this?
We’ve all experienced "vacation stress"—the kind where you spend more time planning the trip than actually enjoying it.
The Problem: Standard travel sites give you a generic list of sights. They don't care if you're on a budget, if you have a student discount, or if you actually like walking.
The Goal: We wanted to create a functional prototype of an AI agent that builds a perfectly optimized, realistic 2-day trip in Germany based on your life.
🤝 The Process: A Story of Collaboration
During this high-energy workshop, we split into two squads to maximize our speed:
Frontend & Product (my squad): We focused on the human side. Using Lovable, we rapidly iterated on the UI/UX. It allowed us to move from "What if we added a budget slider?" to a working feature in minutes.
Backend & Brains: They handled the heavy lifting. Using Tensorix (LLMs) and n8n (Agent Orchestration), they built the logic that actually "thinks" like a local guide.
The Pitch: After a weekend of sprinting, we pitched our working agent to the workshop participants, showing exactly how AI can take the friction out of travel.
🛠️ How it Works: The "Magic" Features
We didn't just want a list of links; we wanted a smart itinerary.
Step Goal Integration: Tell the app how much you want to walk. It automatically calculates if you should take the U-Bahn or if a scenic walk fits your energy levels.
Local Intelligence: The app understands German specifics like the Deutschland-Ticket (D-Ticket) and ISIC Student Discounts to give you a true cost estimation.
Weather-Aware: Includes filters for rainy days (prioritizing museums and indoor culture) and "Free Only" options for budget travelers.
TSP Optimization: We used a Traveling Salesperson Problem algorithm to ensure the route is geographically logical—no more zig-zagging across the city!
✨ The Outcomes
Personalized Itineraries: A minute-by-minute 2-day plan generated in under 30 seconds.
Realistic Budgeting: Not just a guess, but a calculated total including food, transport, and entry fees.
Modern UX: A polished, responsive interface that feels like a premium product.
⚙️ The Tech Stack
Frontend: React, TypeScript, Tailwind CSS, and Framer Motion (UX accelerated via Lovable).
Backend: Tensorix (LLM/ML), n8n (Workflow/Agent Automation).
Mapping: Leaflet & React-Leaflet for interactive city exploration.
Storage: Supabase for managing trip data and preferences.
“Trip-it isn’t just an app; it’s proof that when you combine AI agents with a focused team, you can turn a weekend workshop into a product that solves a real-world headache.”