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QQuote – Automotive Parts Quotation Platform QA [Canada] | Jan 2024 – Aug 2024 | NDA Protected
Overview: B2B web platform for Canadian auto dealerships to generate accurate quotations for tires, accessories, vehicle parts using vehicle-compatibility algorithms (year/make/model/trim).
My Role: QA Engineer
Work I Did:
Reviewed business requirements and created 80+ test cases: Functional, Smoke, Regression, E2E
Validated pricing logic: tiered pricing, dealer markup, discounts, tax calculation, fee handling - found critical bug where winter tire pricing wrong for SUV category
Vehicle-compatibility testing: Verified make/model/year coverage for 1000+ vehicles, OEM vs aftermarket parts matching, fitment validation - ensured correct parts suggested
Quotation workflow: Draft → Final → Checkout - tested save, edit, duplicate, expiration logic
Automated repetitive dashboard validations using Selenium WebDriver JavaScript - automated 40 test cases for pricing dashboard, reducing daily verification from 2 hours to 15 mins
Cross-browser testing on Chrome, Firefox, Safari via BrowserStack
Bug lifecycle in Jira: 35+ bugs reported with clear reproduction, severity/priority, collaborated with devs through resolution
Impact: Improved pricing accuracy from 92% to 99.5%, reduced manual verification effort by 85%
Tools: Selenium JavaScript, Manual Testing, Jira, BrowserStack, Chrome DevTools
Domain: Automotive B2B | NDA: Approach only
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Clients often start with the same request: "Let's build an AI agent."
My counter-question is always: which manual process actually costs you time, errors, or money right now?
My selection rule: remove it first. If it can't be removed, simplify it. If it's still needed, automate it with a plain script. Only then add an integration or an agent. Often the best result is the process that no longer exists.
Safe automation means real scenarios, error handling, and alerts. Without alerts, a workflow can silently lose data for weeks. I always define what stays with a human and what goes to the system.
Question for you: which process would you automate first, and why is it still manual?
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Your Logistics Team Shouldn't Have to Chase Every Shipment Manually. 🚢
A shipment is delayed. Your customer wants an update. Your operations team is checking carrier portals, copying tracking numbers into spreadsheets, and sending the same status emails repeatedly.
By the time everyone has the right information, the next shipment needs attention.
The problem isn't a lack of effort. It's disconnected logistics operations.
Common challenges:
Shipment updates scattered across multiple carrier systems.
Delayed shipments discovered too late.
Manual customer status emails.
Tracking spreadsheets that quickly become outdated.
Repetitive follow-ups between operations and customer service.
Limited visibility into shipment exceptions.
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Instead of manually checking every shipment, your team receives structured updates when something actually needs attention.
What the automation handles:
📦 Shipment Tracking — Retrieve updates from supported carrier APIs.
🚨 Exception Detection — Identify delays, customs holds, and missed milestones.
🤖 AI-Assisted Updates — Convert complex tracking events into clear, customer-friendly messages.
📧 Customer Notifications — Send relevant shipment updates automatically.
📊 Operations Visibility — Maintain synchronized shipment records and exception histories.
🔁 Escalation Management — Notify the responsible team when an exception requires action.
The goal: Less repetitive tracking work, faster exception awareness, and more consistent customer communication.
Two ways we can work together:
Option 1: Already have carrier APIs, a TMS, or a defined tracking workflow? Share your requirements, and I'll help design the integration.
Option 2: Not sure which logistics tasks should be automated? We can map your shipment lifecycle and identify the highest-value starting point.
Make your logistics operations easier to manage as shipment volume grows.
Workflow architecture
Scheduled Trigger → Carrier API Request → Normalize Tracking Events → Match Shipment Records → Detect Status Changes → Route Exceptions → AI Summary → Customer Email / Operations Alert → Database Update
Implementation: Authenticate carrier integrations, normalize differing event codes, compare timestamps and event IDs, prevent duplicate alerts, route exceptions by severity, and log delivery failures for retry.
Benefits: Reduced manual tracking, clearer exception ownership, more timely customer updates, and an auditable shipment history.
Result: A centralized shipment monitoring workflow that allows operations teams to focus on resolving exceptions instead of repeatedly checking tracking portals.
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The customs-hold exception is the one I'd find hardest to route, since it needs a different owner than a plain delay. Do you map each exception type to its own team in the escalation step, or is it one shared alert queue?