QSell Case Study

# Product Model with Boosting Logic
class Product(models.Model):
    title = models.CharField(max_length=200)
    is_boosted = models.BooleanField(default=False)
    boost_start = models.DateTimeField(null=True)
    boost_expiry = models.DateTimeField(null=True)

    def activate_boost(self):
        now = timezone.now()
        self.is_boosted = True
        self.boost_expiry = now + timedelta(days=30)
        self.save()

    @property
    def is_boost_active(self):
        return self.is_boosted and self.boost_expiry > timezone.now()

Built for Scale

QSell was designed with a focus on data integrity and performance. By leveraging PostgreSQL's advanced querying and Django's robust ORM, the platform handles complex filtering and search across thousands of listings effortlessly.

01.
Cloudinary Integration
Automated image optimization and global CDN delivery for lightning-fast marketplace browsing.
02.
Real-time Messaging
Integrated buyer-seller chat system that facilitates faster deal closures and trust-building.
03.
Monetization Engine
Sophisticated ad-boosting system integrated with Razorpay for revenue generation.

Platform Highlights

💬

Unified Chat

A seamless communication layer that allows buyers to inquire about products and negotiate prices in real-time, directly from the product page.

🚀

Ad Boosting

A powerful feature for sellers to increase visibility. Payments are processed via Razorpay, instantly promoting the ad for a 30-day period.

📊

Admin Insights

A custom-built administrative dashboard that provides detailed analytics on user registration, product trends, and pending reports.

📱

Responsive UX

Designed with a "Mobile-First" approach, ensuring that listing and buying products is as easy on a smartphone as it is on a desktop.

Interface Showcase

Marketplace Hero & Search

Category Grid Navigation

Future-Proofing the Marketplace

Beyond just a listing site, QSell incorporates security features like email OTP verification and report-tracking to ensure a safe environment for all community members. The modular codebase allows for easy integration of future features like bidding systems or location-based recommendations.