THE CATALYST
A $250 Trip to Meghalaya That Took 2 Months to Plan
How a real budget planning crisis exposed the massive fragmentation in travel tools
01 THE PERSONAL SPARK
I planned a budget solo trip to Meghalaya . But the planning process was exhausting. I spent weeks jumping between YouTube videos, Google Maps, local homestay blogs, and Excel sheets to calculate route distances and costs . Drowning in cognitive fatigue, I realized travel planning is fundamentally broken and decided to build SoraPath AI to solve my own friction.
02 VALIDATION OVER JARGON
Instead of inventing fake, over-polished user personas for the sake of academic completeness, I ran deep Q&A sessions with 3 real solo budget travelers. Their feedback confirmed my pain point was universal: travel planning is broken because users are forced to manually bridge the gap between separate, disconnected tools just to make a single cost-or-time decision.
KEY INSIGHT
Travel planning wasn't hard because of one problem. It was difficult because users had to switch between multiple disconnected tools before making a single decision.
This insight shaped the foundation of SoraPath AI to simplify, automate, and manually bridge end-to-end traveler experience.
DECISION STORY 1
Designing for Information Density
How I structured a modular, three-pane layout to solve "tab-hell" while preventing cognitive fatigue.
01
02
03
01
PLANNING ZONE
Set the foundation for your trip.
Trip Preferences
Summary Cards (Cost, Distance, Duration, Best Time)
02
DECISION ZONE
Make informed travel decisions.
Route Overview (Map)
Budget Breakdown
AI Recommendations
03
EXECUTION ZONE
Plan your days with confidence.
Nearby Suggestions
Itinerary Snapshot
Weather Outlook
ONE UNIFIED WORKSPACE
From research to ready-to-go plan, SoraPath AI keeps everything organized so you can focus on the journey, not the planning.
DECISION STORY 2
Demystifying AI through Financial Transparency
How I designed an interactive, real-time budgeting engine that transforms AI planning from a mysterious 'black box' into a transparent, user-controlled collaborator.
Our AI analyzes your trip, destination costs and preferences to build a realistic and optimized budget plan.
BUDGET BREAKDOWN (MEGHALAYA TRIP)
The Trust Deficit (The Friction)
The AI Trust Problem During my 3 user Q&A sessions, I discovered a major psychological barrier: travelers don't trust automated AI recommendations because they don't understand the financial math behind them. They worry about hidden costs, expensive transport estimates, and unrealistic homestay prices. To build a product users would actually trust, the UI had to provide immediate, visual proof of how every Rupee is allocated.
The Budget-Comfort Slider (The Solution)
The Interactive Budgeting Engine To solve this, I designed and coded an interactive budgeting engine. Instead of forcing users to accept a static cost estimate, I introduced a responsive 'Budget vs Comfort Level' slider. Moving the slider dynamically recalculates the entire itinerary in real-time—instantly swapping high-end homestays for budget-friendly rooms and private cabs for shared transport, while keeping the user in full control.
Real-Time Verification (The Loop)
Immediate Visual Feedback I mapped the slider's parameters directly to a live, categorized pie chart. This gives travelers total financial transparency over their expenses (Transport, Stay, Food, Activities) at a glance. By allowing users to visually verify how their choices impact their wallet, SoraPath AI reduces checkout anxiety and drives higher booking intent.
HOW I OPTIMIZED THE BUDGET
1. User Intent Capture
Designing lightweight, friction-free input forms to collect travel style preferences
Real-Time Processing
Leveraging Python on the backend to dynamically parse destination seasonality and travel modes
Smart Resource Mapping
Structuring the UI layout to instantly adapt when budget-conscious parameters are triggered
4. Frontend Integration
Coding the responsive comfort sliders in React to ensure zero-latency visual feedback
5. Frictionless Handoff
Outputting a beautiful, shareable, budget-aligned itinerary ready to print or book
Reflection & Future Vision
Every product is a journey of learning, iteration and growth. Here's what I learned while building SoraPath AI and where it's heading next.
1. BIGGEST CHALLENGES
Designing for Information Density
Balancing a single-screen dashboard integrating maps, interactive timeline cards, and real-time budgets without causing cognitive fatigue
AI with User Control
Preventing the AI from feeling like a rigid 'black box.' I had to design transparent controls (like the Comfort Slider) so users collaborate with the AI rather than blindly trusting a static output
Building a Recommendation Engine
Formulating a scalable logic to parse travel preferences and map API variables, ensuring that complex itineraries load without layout shifts
Maintaining Consistency
Aligning my high-fidelity Figma component library directly with custom Tailwind CSS variables to guarantee visual parity during frontend coding
2. KEY LEARNINGS
Users do not trust blind automated suggestions. Designing clear, real-time interactive feedback loops (like the budget pie chart) is what actually builds product trust
Hand-coding the responsive layouts in React made me a better designer. Understanding container rules forced me to structure highly disciplined Figma systems
A rigorous design system prevents layout tech-debt. Grounding SoraPath’s interfaces on strict tokenized variables saved me weeks of frontend implementation time
Structural spatial hierarchy is the ultimate UX solution. Correctly dividing the workspace into distinct planning, decision, and execution zones solves planning anxiety better than any layout animation
3. FUTURE ROADMAP
PHASE 1
Foundation
Hotel Booking APIs
Flight Integration
Detailed Destination Database
Currency & Visa Info
Short Term
PHASE 2
Collaboration
Collaborative Trip Planning
Shared Itineraries
Group Budgeting
Real-time Sync
Mid Term
PHASE 3
Intelligence
Real-time Weather Adaptation
Live Budget Tracking
Offline Mode
Smart Notifications
Long Term
PHASE 4
AI Evolution
Multi-city AI Planner
AI Travel Assistant
Voice-based Planning
Predictive Suggestions
Future Vision
AI ENHANCEMENTS IN FOCUS
Smarter recommendations with personalization
Conversational AI for trip planning
Real-time data for better decision making
Context-aware suggestions across the journey
PROJECT AT A GLANCE
Role
Product Designer & Full-Stack Developer
Duration
Jan 2026 – June 2026
(6 Months)
Platform
Desktop-first
Web Application
Tools & Technologies
Figma, React, Tailwind, Python, FastAPI, OpenAI, Gemini, Map APIs, JSON Data
Focus Area
Product Design, AI Recommendation Engine
, Itinerary Generation
My Contribution
End-to-end product design,
AI system architecture,
Frontend implementation & Backend APIs
SoraPath AI was designed to transform travel planning from hours of fragmented research into one intelligent, personalized experience.
Designed & Developed by
Sourav Haldar
Crafting meaningful experiences that solve real problems
Thank you for taking the time to explore the SoraPath AI case study.
I hope this gives you a clear picture of how research, design thinking, and AI can come together to build meaningful travel experiences.