Maply
A calm, AI-powered travel planning platform that transforms social inspiration and scattered notes into route-optimized, human-approved itineraries.
Nobody starts planning a trip with a blank itinerary. They start with scattered inspiration and no clear way to act on it. Maply is a platform designed to respect how people actually plan trips, providing route optimization and context-aware recommendations while keeping the user in control.
By treating your hotel as an anchor point, Maply helps minimize travel distance and backtracking, automatically calculating efficient day paths and serving intelligent, web-scraped recommendations tailored to your traveler profile.
Role
Design & Dev
Full-stack creator
Timeline
2 Months
Core MVP shipped
Focus
Route Optimization
Path-based logic
Status
Active Dev
Continuous updates
01 — The Problem
Scattered Inspiration Meets Blank Itineraries
Most travel planning tools fail because they assume you start organized. They open on a cold, blank calendar page and expect you to know what to write.
I've watched friends plan trips. I've watched myself plan trips. And I noticed something consistent: nobody starts with a blank itinerary.
They start with Instagram saves. YouTube videos. A friend's recommendation in a text message. A Reddit thread. A blog post. It's messy. It's scattered. It's everywhere.
"I have 47 saved Instagram posts for Tokyo, 12 YouTube videos, and a Google Doc with random notes. Now what?"
This is where traditional planners collapse. They lack a bridge to go from messy inspiration to an actionable plan. People collect inspiration first. They organize later.
And when they finally sit down to organize? They face another problem: route optimization.
Traditional planners show you what's "nearby" using radius circles. But you don't teleport. You walk, drive, or take trains. You follow paths, not circles. Radius circles lead to backtracking, high travel times, and exhausted travelers.
02 — The Solution
Six Principles of Calm Planning
Maply was built around a simple belief: respect how people actually plan trips. These six core principles guide every single design and feature decision.
Inspiration First
Start where planning begins—with messily collected ideas from Instagram, YouTube, notes, anywhere.
→ Because nobody opens a blank page and knows what to write.
Hotel as Anchor
All routes reference where you stay, providing a central point for all travel and routing logic.
→ Your hotel is the one constant. Everything else radiates from there.
AI Suggests, Humans Decide
Every AI extraction is editable and confirmable. No hidden magic without direct user control.
→ AI should assist, not dictate. Trust comes from absolute transparency.
Paths > Proximity
Route-based suggestions that actually make sense for physical travel, not just radius circles.
→ You follow roads, not straight lines. Real travel has detours and routes.
Calm Over Completeness
Low cognitive load experience. No cluttered dashboards or overwhelming feature sets.
→ Planning a trip should reduce stress, not create it.
Editability Over Magic
User control is paramount. AI acts as a helpful copilot, but the user owns the final plan.
→ Your trip, your choices. Always.
03 — Design & Journey
Apple Notes meets Apple Maps
A neutral, spacious, typography-driven visual system built entirely using the Zinc palette. Grayscale interfaces perform significantly better for complex AI products because they reduce cognitive noise and let the content breathe.
Create Trip
Set your destination, choose your hotel (the anchor), define dates, select traveler type (solo, couple, friends, family), and set your daily pace.
The hotel isn't just a place to sleep—it's the center of your entire trip. Every route calculation, every suggestion, every optimization starts from there.
Add Places
Three ways to build your list: AI Suggestions (trending places for your context), Search & Add (Google Places), or Extract (paste text/URLs or upload files).
*AI Extraction pipeline:* Paste your Instagram saves, upload that PDF guide, or drop in YouTube URLs. The system extracts the place names, validates them against Google Places, and populates your dashboard instantly.
Organize Days
Auto-grouping based on trip duration. Timeline view showing Hotel → Place 1 → Place 2 → ... → Hotel. Drag-and-drop reordering with route efficiency feedback.
See your day as a path, not a list. Minimize backtracking, understand the flow, and optimize for real-world travel times.
View Itinerary
Final optimized plan with full details, clear route visualization, and an exportable format ready for the road.
Your messy inspiration is now a structured, optimized, highly actionable travel plan.
Technical Architecture
The Maply Tech Stack
Building Maply meant solving problems like AI extraction from unstructured text, real-time route optimization, contextual AI filtering, and high-fidelity place validation.
Frontend
- •Next.js 16 (App Router)
- •TypeScript for type safety
- •Tailwind CSS (Zinc Palette)
- •shadcn/ui components
Intelligence Layer
- •Google Gemini Flash (AI)
- •Serper API (Web Search)
- •Hugging Face & OpenAI
- •Tesseract.js (OCR)
Maps & Location
- •Google Places API
- •Google Directions API
- •Google Maps JS API
- •Haversine formula calculations
Data & Files
- •pdf-parse (PDF extraction)
- •mammoth (DOCX parsing)
- •localStorage (Client state)
04 — Engineering
Solving the Hard Problems
Pragmatic, high-performance engineering designed to solve real-world traveling hurdles. Here is how I tackled the main technical roadblocks.
AI Place Extraction from Unstructured Content
Users paste anything—Instagram captions, blog posts, YouTube descriptions, or upload messy PDF guidebooks. How do you reliably extract exact place names?
Before: Manual Keyword matching
Keyword lookup. Failed for complex, localized names like "Bashundhara City Shopping Complex" because it did not match any static categories.
After: Multi-layer pipeline
Confidence-scored extraction (Regex → OpenAI/Hugging Face → Zero-shot classifier) backed by Tesseract OCR and verified instantly against Google Places API.
Don't reinvent the wheel. Google knows what every place is. Your job is to orchestrate existing systems intelligently, not rebuild them.
Route-Aware Place Discovery
Traditional travel applications perform radius-based "nearby" searches. But you don't travel in circles—you travel along paths. How do you find places that fit on your route?
Before: Radius circle search
Showed places in a circular boundary. A restaurant 2km away from the current route point might trigger a 20-minute backtrack detour.
After: Route-detour algorithm
Calculates true detour costs. For any route point A→B, it pre-filters via Haversine segments and suggests places within X minutes of a realistic detour path.
Perfect is the enemy of good. A greedy Nearest-Neighbor algorithm beat a perfect TSP solver in performance, keeping wait times under 1 second.
Contextual AI Filtering
A couple planning a romantic trip does not want to see primary schools or generic corporate office towers. A family doesn't need nightclubs. How do you filter places intelligently?
Before: Static keyword filters
Hardcoded categories that failed to capture nuances (e.g. including a business district restaurant for a couple's quiet dinner).
After: Gemini-powered context filter
Google Gemini-powered classification. Matches traveler type, speed pace, and historical traveler reviews to rank relevance automatically.
Building for graceful degradation is vital. If AI services are unavailable, the app falls back to structured API mapping seamlessly without breaking.
Place Ordering & Route Optimization
Given 10 places and a hotel, what is the optimal travel order to minimize total transit time and avoid zigzagging across the city?
Before: Manual Ordering only
Left all order adjustments entirely to the user. Resulted in high backtracking and massive frustration when managing more than 5 places.
After: Nearest-Neighbor anchor solver
Uses the hotel as the start/end anchor. Calculates distance matrices dynamically to layout an optimal path, while letting users override via drag-and-drop.
Real travel order requires flexibility. A rigid mathematical solution often frustrates users if they have subjective preferences; mixing auto-optimizations with manual override is key.
05 — Intelligence
Scraped, AI-Analyzed, Context-Aware Recommendations
Standard travel apps fetch generic point-of-interest lists from APIs. Maply parses real traveler conversations across Reddit, blogs, and social platforms to recommend genuine local favorites.
Before: Generic API lists
- • "Racha Enterprise Building" (office tower)
- • Secondary schools (on a romantic trip)
- • Bland, generic tourist shopping malls
- • Standard fast food chains
After: Socially-vouched AI spots
- • Back-alley ramen shops extracted from local subreddits
- • Boutique scenic rooftops with zero commercial crowd
- • Real-time validation ensures spots are active
- • Matched specifically to individual traveler paces
Web Scraping
Serper API searches Reddit, forums, and blogs to extract real traveler recommendations.
AI Analysis
Gemini AI reads results, filters out noise, extracts places, and writes helpful rationale.
Context Filtering
Intelligent demographic logic. Nightlife for solos, parks for families.
Example AI Reasoning Output
"Octave is consistently mentioned on Reddit r/Bangkok as the best rooftop bar for solo travelers, with stunning 360° views and a relaxed vibe perfect for meeting other travelers. Highly rated on TripAdvisor (4.6★) and featured in Timeout Bangkok's 2024 nightlife guide."
Every place recommendation includes social proof details and reasoning.
06 — On-The-Road
In-Trip Adaptability
Planning is one thing. Executing is another. Real travel requires constant, stress-free adjustment as parameters change.
Mark as Visited or Skipped
Track journey progression. Checking off a place dynamically advances the route timeline.
Visual progress bar details visited/skipped/upcoming places
Re-Optimize Route Instantly
Skipped a restaurant or museum? Maply automatically re-routes active waypoints to minimize travel time.
Instantly solves backtracking on remaining itinerary items
Nearby Alternatives
If a selected attraction is closed or fully booked, request a matched substitute instantly.
Never lose vacation momentum—always have a calibrated backup plan
Day-by-Day Navigation
Switch between travel days seamlessly. Your active progress remains securely saved.
Persistent client state saves automatically to localStorage
07 — Reflection
What Makes Maply Different
I didn't build another bloated travel manager with complex matrices. I built the calm, lightweight travel planner I wish existed.
Traditional Planners
- Start with blank itinerary
- Manual research required
- Radius-based 'nearby' search
- Generic, commercial suggestions
- Static, un-adaptive plans
- Feature-heavy, cluttered dashboards
Maply Space
- Start with your raw inspiration
- AI extracts from any source
- Route-aware detour calculations
- Context-aware AI recommendations
- Dynamic on-the-road re-optimization
- Calm, minimal, zinc interface
Inspiration First
Zero friction starting point. Pulls data from notes, links, or documents instantly.
Route-Aware
Keeps physical travel logical. Detour-aware matching avoids massive backtracking.
In-Trip Agent
Acts as a live on-trip copilot. Route recalculations adapt on-the-fly.
Zinc Interface
Apple Notes meets Apple Maps. Grayscale aesthetics keep planning calm.
"Inspiration first. AI assists. Humans decide."
This is the philosophy driving every layout decisions, every API integration, and every line of code.
Experience Maply
Currently in active development. Building the future of calm, intelligent travel planning.