AI Travel: Smart Trip Planner & Itinerary Builder

About the project

Swift Wash is an AI-powered on-demand laundry delivery platform designed to reduce friction in the garment care process. The client faced significant customer anxiety around order tracking and pricing transparency. We redesigned the entire mobile experience with a focus on simplicity, real-time updates, and reassurance through micro-interactions. The project increased order completion rates 79%, reduced support inquiries 84%, and achieved 68% repeat order rate—the highest in the laundry delivery category.

Problems

On-demand laundry services suffered from critical trust and clarity issues: order tracking was unclear or missing entirely, pricing breakdowns appeared only at the end (causing abandonment), users couldn't track where their clothes were, communication was inconsistent with unclear delivery windows, and the post-pickup experience was anxiety-inducing with no updates. User research revealed 71% of first-time users never ordered again due to anxiety about their garments. Support inquiries primarily involved "Where is my order?" and "Why am I being charged this?"

Challenges

The core challenge was designing an interface that reduced anxiety through transparency and reassurance. Users weren't worried about laundry logistics; they were anxious about their belongings. We needed to create continuous communication throughout the process, make pricing immediately clear, and provide visual confirmation that garments were being cared for. A secondary challenge was simplifying a complex backend process (pickup scheduling, sorting, cleaning, quality control, delivery) into intuitive frontend experience.

Our Approach

We implemented a transparency-first methodology, beginning with extensive research on service anxiety and on-demand delivery expectations. Our design strategy centered on continuous reassurance: clear upfront pricing, real-time location tracking, step-by-step process visualization, and friendly communication. We designed a dashboard showing current order status with live updates, integrated GPS tracking, and a notification system keeping users informed proactively rather than requiring them to check app status. The methodology involved designing status flows for every possible scenario (on time, delayed, issue detected), testing with 100+ users, and optimizing for anxiety reduction rather than information density.

Project Timeline

Research & Service Mapping (Weeks 1–3): User interviews (50+ laundry service users), service experience mapping, anxiety point identification, competitor analysis.

Experience Design & Flow Mapping (Weeks 4–6): Three-step ordering flow design, status tracking visualization, notification strategy, error handling scenarios.

High-Fidelity UI Design (Weeks 7–10): Dashboard design, order tracking interface, notification design, micro-interaction specifications.

Integration & Prototyping (Weeks 11–12): Real-time backend integration testing, GPS tracking visualization, notification system testing.

Testing & Optimization (Weeks 13–14): Usability testing with 80+ users, anxiety metric measurement, support inquiry analysis, performance optimization.

Sketch

Early sketches explored different conversation layouts — from classic chatbot styles to a card-based interface blending dialogue with visual suggestions. Iteration tested itinerary views (timeline, day-by-day, and map-based) and destination card designs balancing imagery with clarity. User testing showed travelers preferred visual confirmations (photos, maps) before booking, shaping the final hybrid approach and in-app booking flow.

Style Guide

The design expressed care and reliability using fresh blues (#0EA5E9), warm neutrals (#F5F1E8), and supportive greens (#22C55E). Rounded DM Sans type added friendliness. Icons and illustrations humanized the process—each stage symbolized by simple, meaningful visuals.

Wireframe

Low-fidelity wireframes mapped a clear flow: home dashboard, three-step order, live tracking, and feedback. Key focuses: visible pricing, timeline-based progress, and contextual notifications. Modular cards and maps improved usability and scalability.

Animations

Micro-interactions added reassurance—animated pricing updates, smooth tracking transitions, and subtle success confirmations. Motion timing (200–500ms) kept feedback natural and professional, emphasizing trust and care without excess.

Multi screen

The app featured 28 optimized mobile-first screens: onboarding, dashboard, order flow, tracking, history, and support. Layouts prioritized map visibility and quick reference for active orders.

Visual Identity and Brand Story

Swift Wash positioned itself as a “caring garment partner”—not just a delivery app. Real imagery and friendly tone built emotional connection. The logo and visuals symbolized movement, trust, and care, making users feel their clothes were truly looked after.

Results & Outcomes

Post-launch, order completions rose 79%, support queries fell 84%, and NPS jumped from 22→67. Repeat orders hit 68%, and average order value grew 31%. Key insight: users value emotional reassurance and transparency more than speed—trust builds loyalty.