Final Year Project · Flutter · Firebase · GPS · Machine Learning
LaundrOTrack
An integrated on-demand laundry service platform connecting customers, drivers and administrators through digital ordering, real-time delivery tracking, Firebase operations and intelligent customer assistance.
Product demonstration
See the complete workflow.
The walkthrough demonstrates the customer ordering journey, driver delivery operations, live tracking and the administrator management experience.
Research overview
From fragmented coordination to one connected service.
Developed as a 2026 Bachelor of Computer Engineering Final Year Project, LaundrOTrack addresses manual ordering, unclear service progress, limited delivery visibility and inefficient communication.
The problem
Phone calls, walk-ins and external messaging can create unclear order details, inconsistent pickup times, weak order visibility and repeated manual follow-up.
The solution
A unified digital ecosystem for ordering, pickup, processing, return delivery, live monitoring, notifications and customer support.
The contribution
A modular multi-role platform integrating Flutter applications, Firebase services, Google Maps and a Python machine learning API.
Project objectives
Three engineering goals.
Unify the service
Design, implement and evaluate customer, driver and administrator functionality within one platform.
Track every journey
Implement and evaluate real-time location monitoring for pickup and return delivery events.
Automate assistance
Integrate a machine learning conversational assistant for context-aware laundry service enquiries.
Role-based platform
One system, three focused experiences.
Book and stay informed
- Registration, login and profile management
- Service selection, pickup address and payment flow
- Order timeline, notifications and live tracking
- Laundra Ask and administrator support chat
Manage pickup and delivery
- Pickup and return delivery request queues
- Task acceptance, rejection and active orders
- Route navigation and live location sharing
- Status updates, availability and task history
Operate from one command centre
- Order, return dispatch and user management
- Driver assignment and live monitoring
- Operational analytics and notifications
- Customer support and access control
System architecture
Mobile, cloud, maps and machine learning.
A modular architecture connects the Flutter interfaces to a shared Firebase backend, Google location services and the independently deployed Laundra Ask prediction API.
Flutter + Dart
Cross-platform customer and driver applications plus the administrator dashboard interface.
Firebase
Authentication, Cloud Firestore, Cloud Messaging, Storage, Functions and Hosting.
Google Maps + GPS
Live coordinates, routes, markers, distance calculation, ETA and arrival detection.
Python + Scikit-learn
TF-IDF and Linear SVC intent classification served through a Flask API on Render.
Core capabilities
Engineered across the complete workflow.
Real-time synchronization
Orders, driver locations, status updates, notifications and chat records remain connected through Cloud Firestore.
Role-based access
Firebase Authentication and stored user roles provide the correct experience for customers, drivers and administrators.
Route and ETA
Google Maps displays route polylines, destination markers, distance and estimated arrival information.
Arrival detection
Location-based logic detects pickup and return delivery arrival events around the active destination.
Automated notifications
Firebase Cloud Messaging and Cloud Functions support timely operational updates across the platform.
Laundra Ask
A saved Scikit-learn pipeline classifies English, Malay and mixed-language enquiries across order, ETA, price, payment, pickup and delivery intents.
Development methodology
A structured modified-waterfall process.
Requirements
Investigated manual laundry operations, existing systems, tracking research and user responsibilities.
System design
Produced architecture, use cases, module flows, database structures and notification designs.
Implementation
Built Flutter modules, Firebase services, Google Maps integration and the Laundra Ask model.
Integration
Connected customer, driver and admin workflows to one synchronized backend environment.
Functional testing
Investigated each module, tracking, notifications, chatbot behaviour and support chat synchronization.
Evaluation
Reviewed objective achievement, system outputs, project limitations and commercial expansion paths.
Evaluation outcome
All three project objectives were achieved.
Functional evaluation confirmed the integrated role workflows, real-time GPS tracking and Laundra Ask machine learning assistant. Classification reports, a confusion matrix and sample intent predictions were used to evaluate the chatbot. The resulting system provides a structured journey from order placement through return delivery while improving visibility and reducing dependence on manual communication.
Integrated platform
Live location tracking
ML customer assistant
Product gallery
Inside the LaundrOTrack experience.
Selected implementation screens from the completed FYP2 report and original product designs.
01 / Customer app
Everything starts from one calm dashboard.
Customers can confirm their pickup location, see active orders and move directly into booking or tracking without searching through complex menus.
02 / Order records
A complete history, always within reach.
Every transaction is presented with its order number, service, price and current state so customers can understand past and ongoing laundry activity at a glance.
03 / Progress timeline
From booking to return delivery, clearly explained.
A milestone-based timeline makes the service journey visible, reducing uncertainty while clothes move through payment, pickup, processing and final delivery.
04 / Smart ordering
Services, load size and add-ons in one guided flow.
The booking flow breaks a detailed laundry order into understandable choices, while maintaining a running total before the customer confirms the basket.
05 / Confirmation
A confident finish to every checkout.
The confirmation screen gives immediate reassurance with an order reference and concise service summary, followed by a direct path into tracking.
06 / Driver lounge
One workspace for every pickup and return task.
Drivers can set their availability, switch between pickup and return queues, review customer details and manage active jobs from a focused operational view.
07 / Navigation
Live routes with distance and arrival intelligence.
Google Maps coordinates, route polylines and ETA calculations help drivers navigate efficiently while arrival detection keeps the service state synchronized.
08 / Admin command centre
The entire operation in one view.
Administrators can monitor revenue, orders, drivers and service activity through a dashboard designed for fast daily decision-making.
09 / Live supervision
Every active journey, visible in real time.
The command centre connects the active driver map with journey information, enabling administrators to supervise pickup and delivery progress as it happens.
10 / Laundra Ask
Helpful answers, powered by machine learning.
The multilingual assistant classifies customer intent across pricing, orders, ETA, payment, pickup and delivery questions to provide immediate guidance.
11 / Human support
When automation is not enough, a person is close by.
Real-time customer and administrator messaging creates a direct support channel while preserving the order context needed to solve issues quickly.
12 / Driver onboarding
Ready for work in a few focused steps.
Drivers maintain availability, personal information and vehicle details in one profile, creating better task matching and clearer operational records.
13 / Notifications
Important updates arrive at the right moment.
Automated notifications keep customers informed about collection, processing, assignment and delivery without requiring repeated manual follow-up.
Recognition
Research turned into award-winning innovation.
Limitations and roadmap
Designed to grow beyond the prototype.
- Improve tracking resilience in weak GPS and unstable network conditions.
- Expand Laundra Ask with more real customer questions and intent data.
- Add driver-admin communication and flexible delivery preferences.
- Introduce payment gateway, cancellation and automated refund workflows.
- Scale into a multi-laundry marketplace with independent delivery drivers.