Project

Last-Mile Delivery Platform

Optimizing Real-Time Deliveries & Tracking Accuracy for Last-Mile Logistics Platform

Client type: Delivery Technology Startup
Duration: 6 Months
Team size: 1 QA Lead + 3 QA Engineers + 1 Automation Tester

Overview

Validates that AI generates efficient, multi-stop routes (up to 1,000+ stops) while considering traffic, vehicle capacity, and time windows. Tests performance on mobile devices, including real-time route updates, proof-of-delivery (photo/signature), barcode scanning, and navigation.

Client overview

About client

A logistics platform managing same-day deliveries for eCommerce brands with rider allocation and live tracking.

Platform

Web Admin, Android Rider App, Customer Tracking Web

Users

2,000+ daily deliveries

Problem statement

The client faced delayed status updates, failed rider assignments, and route sync issues impacting delivery SLA performance.

Challenges

Real-time tracking delays
Rider allocation failures
Address geolocation mismatch
Peak-hour app slowdowns

Our solution (testing approach)

Tracking API validation
Assignment engine testing
Mobile device compatibility testing
Load testing for peak delivery hours

Tools & technologies

Selenium, Appium, Postman, JMeter, BrowserStack, JIRA

Results & metrics

61% better tracking accuracy
46% faster rider allocation
28% improvement in on-time deliveries

Process

1

Requirement Analysis

Review delivery workflows
2

Automation Setup

Build dispatch regression suite
3

Execution

Run mobile and API tests
4

Performance Testing

Simulate peak delivery traffic
5

Release QA

Final go-live validation

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Java Selenium Web Automation

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The project turned out to be more complex than we thought initially, but Hfinity India took extra effort to make it work. I am sure I would love to work with him again. They did an amazing job on a complex project for the Cloud-based software QA platform

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Mobile Testing

From API contracts to mobile builds, the team speaks our stack. Communication was transparent and timelines were predictable.