E-Commerce AI Case Study | Kawach Technology

Client Overview

Urban Threads Apparel

Urban Threads Apparel had built a genuinely loyal D2C fashion customer base, but their online store had a problem that only showed up at the worst possible moment: every major flash sale or festive campaign — precisely the events driving the most revenue of the year — the site slowed to a crawl or crashed outright. Customers gave up and left, and the team could only watch it happen in real time....

Industry

Retail & E-commerce · Retail Software Development

Business Size

₹15 Cr+ annual online revenue

Location

Delhi NCR, India

Business Model

Direct-to-Consumer (D2C) Fashion Retail

Project Duration

5 months

Existing Challenges

The storefront repeatedly slowed down or crashed during flash sales and festive campaigns — the exact moments driving the most revenue.
Every customer saw the same generic homepage and product listings, regardless of browsing or purchase history.
Inventory counts were updated manually every few hours, leading to overselling during high-traffic sales events.
Cart abandonment sat above 78%, with no automated way to re-engage customers who left without checking out.
Average mobile page load time — where 85% of traffic came from — was over 6 seconds.

The Challenge

What We Were Up Against

Urban Threads Apparel had built a genuinely loyal D2C fashion customer base, but their online store had a problem that only showed up at the worst possible moment: every major flash sale or festive campaign — precisely the events driving the most revenue of the year — the site slowed to a crawl or crashed outright. Customers gave up and left, and the team could only watch it happen in real time.

Beyond the crashes, the storefront treated every visitor identically. A first-time browser and a five-time repeat customer saw the exact same homepage and product listings, with no attempt to surface what either of them was actually likely to buy. Inventory counts were only updated manually every few hours, which meant overselling during high-traffic sales — a customer service nightmare that meant refunding orders the warehouse simply couldn't fulfill.

Cart abandonment sat above 78%, with no automated way to bring those customers back. And because 85% of Urban Threads' traffic came from mobile, the storefront's average mobile page load time of over 6 seconds was quietly costing sales on every single visit, not just during sale events.

Our Solution

How We Built It

We opened with a performance audit, running load tests against the existing storefront to pin down exactly which parts of the stack buckled under pressure — it turned out to be a mix of an undersized database connection pool and no caching layer at all in front of product pages. That diagnosis shaped everything that followed: we re-architected the platform around auto-scaling cloud infrastructure and a Redis caching layer built specifically to absorb sudden traffic spikes.

For personalization, we trained a recommendation model on two years of Urban Threads' own historical browsing and purchase data — deliberately avoiding third-party data brokers — before deploying it live. Real-time inventory sync and automated abandoned-cart email/SMS sequences shipped first, since they delivered value immediately; AI-driven recommendations followed once the core platform had proven stable.

Before trusting any of it with a live festive campaign, we simulated 5x Urban Threads' expected peak traffic in a staging environment and tuned auto-scaling rules until the platform absorbed that simulated load without a single error. Image optimization and CDN delivery were layered in specifically to address the mobile load-time problem, since that's where the overwhelming majority of visitors actually were.

We stayed on real-time monitoring and on-call support throughout Urban Threads' biggest sales campaign of the year — the actual moment of truth the whole rebuild had been aimed at.

Key Modules Delivered

AI Recommendation Engine

A machine-learning model that ranks product recommendations per visitor using browsing history, purchase patterns, and real-time trending items.

Real-Time Inventory Sync

Inventory updates propagate to the storefront within seconds of a sale, across all sales channels.

Abandoned Cart Automation

Automated email and SMS sequences triggered when a cart is left inactive, with time-limited discount nudges.

Auto-Scaling Storefront

Cloud infrastructure that automatically scales server capacity up during traffic spikes and back down afterward.

Image & CDN Optimization

Automatic image compression and global CDN delivery to cut mobile page load times.

Goals & Objectives

What Success Looked Like

Survive Peak Traffic

Rebuild the platform to reliably handle 5x normal traffic during festive sales without downtime.

Personalize the Shopping Experience

Show each customer product recommendations based on their own browsing and purchase behavior.

Fix Inventory Accuracy

Sync inventory across the storefront in real time to eliminate overselling.

Recover Abandoned Carts

Automatically re-engage customers who leave without completing checkout.

Features Developed

What We Built

AI Product Recommendations

Personalized product suggestions based on real browsing and purchase behavior.

Auto-Scaling Infrastructure

Automatically handles traffic spikes during flash sales without downtime.

Real-Time Inventory Sync

Prevents overselling by updating stock instantly across channels.

Abandoned Cart Recovery

Automated email/SMS sequences that bring shoppers back to checkout.

Optimized Mobile Performance

Sub-2-second load times even on average mobile connections.

Technology Stack

Built With the Right Tools

We selected every technology based on this project's real requirements: compliance obligations, scalability needs, and long-term maintainability. No trend-chasing, only battle-tested solutions.

Backend

Laravel 10 PHP 8.2 MySQL Redis

Frontend

React 18 Next.js Tailwind CSS

Search & Recommendations

Elasticsearch Python scikit-learn

Infrastructure

AWS Auto Scaling CloudFront CDN S3

Development Process

How We Delivered It

Agile delivery with regular demos and continuous deployment. Full transparency at every stage.

Total Timeline

5 months

Started → Ongoing

1

01

Performance Audit

Ran load tests against the existing storefront to identify the exact bottlenecks that caused crashes during past sales events.

2

02

Architecture Redesign

Re-architected the platform around auto-scaling infrastructure and a caching layer to absorb sudden traffic spikes.

3

03

Recommendation Model Training

Trained the recommendation model on 2 years of historical browsing and purchase data before deploying it live.

4

04

Phased Feature Rollout

Shipped inventory sync and cart automation first, then layered in AI recommendations once the core platform was stable.

5

05

Load Testing at Scale

Simulated 5x expected festive traffic in a staging environment before the live campaign to validate auto-scaling behavior.

6

06

Live Festive Season Monitoring

Provided real-time monitoring and on-call support throughout the client's biggest sales campaign of the year.

Security & Compliance

Built for the Strictest Standards

Payment Data Security

Checkout is processed through PCI-DSS compliant payment gateways — no raw card data is stored on Urban Threads' servers.

Customer Data Protection

Personal and browsing data used for recommendations is encrypted and access-controlled per internal data policy.

Results / KPIs

Measurable Impact

Numbers measured at 6 months post-launch, independently verified by the client's operations team.

1.8s (from 6s+)

Mobile Page Load Time

99.98%

Festive Season Uptime

-31%

Cart Abandonment

+42%

Conversion Rate

Before vs. After

Before	After
Storefront crashed during high-traffic sales events	99.98% uptime during 5x festive traffic
Same generic homepage shown to every visitor	AI-personalized recommendations per visitor
Manual inventory updates every few hours	Real-time inventory sync across all channels
78%+ cart abandonment with no follow-up	31% reduction in cart abandonment via automation

"

Every festive season used to be a nail-biter — we knew a big sale meant a good chance the site would slow to a crawl right when we needed it most. This time, traffic was five times normal and the site didn't even blink. And the personalized recommendations alone paid for the project within the first quarter.

AN

Ananya Kapoor

Head of E-Commerce, Urban Threads Apparel

★★★★★

Key Achievements

Why This Project Matters

Beyond the numbers: what this project changed day-to-day for Urban Threads Apparel and the people who rely on what we built.

Zero Downtime During Peak Sale

The platform handled 5x normal traffic during the festive campaign with 99.98% uptime and no checkout failures.

42% Conversion Rate Increase

AI-driven product recommendations lifted overall conversion rate by 42% within the first full quarter after launch.

Mobile Load Time Cut by Over 70%

Image optimization and CDN delivery brought mobile page load time down from over 6 seconds to under 2.

FAQ

Common Questions

Have more questions? Book a call with our team.

How did you prepare the platform for festive-season traffic specifically?

We simulated 5x the client's expected peak traffic in a staging environment weeks before the actual sale, tuning auto-scaling rules until the platform handled the simulated load with zero errors.

How does the AI recommendation engine work without being invasive?

It only uses first-party behavioral data — pages viewed, items purchased, items added to cart — collected directly on Urban Threads' own store, with no third-party data brokers involved.

Did the redesign require Urban Threads to change their existing product catalog structure?

No. We built the new platform to migrate the existing product catalog as-is, so the merchandising team didn't need to re-enter or restructure any product data.

Related Case Studies

Scaling Urban Threads Apparel's Online Store for 5x Festive Traffic with AI-Powered Recommendations