🧩 Overview

A growing D2C fashion brand struggled to improve repeat purchases and customer engagement. Their product recommendations and homepage content were static and not tailored to user behavior.

🔍 Challenge

• Low customer retention and AOV (Average Order Value)

• Poor engagement with existing product suggestions

• High bounce rate and limited session duration

🛠️ Tech Stack Used

• Adobe Commerce (backend)

• Clerk.io for AI recommendations

• Mailchimp for segmented email flows

• Google Analytics & Hotjar for behavioral insights

💡 Our Solution

We implemented an AI-powered personalization engine that adapted content in real time based on customer browsing and purchase history.

Key Actions:

• Integrated behavioral data tracking across website sessions

• Set up product recommendation algorithms (upsell, cross-sell, trending items)

• Personalized homepage, cart suggestions, and follow-up emails

• Created customer segments for email automation

🚀 Results

• +22% increase in Average Order Value (AOV)

• +18% increase in conversion rate within 30 days

• Personalized product suggestions drove 34% of total revenue

🧩 Overview

A large healthcare products eCommerce business faced quality issues after each update. Manual testing was slow and couldn’t scale.

🔍 Challenge

• Bugs frequently made it to production

• Manual testing was time-consuming and error-prone

• Regression testing slowed down product releases

🛠️ Tech Stack Used

• Selenium (Java)

• Jenkins CI

• TestNG

• Slack notifications & reporting

💡 Our Solution

We built an automated test suite using Selenium and integrated it with their CI/CD pipeline.

Key Actions:

• Designed over 100 reusable test cases for core eCommerce flows

• Automated checkout, payments, login, and product listing

• Integrated test reports into Slack for instant visibility

• Scheduled automated tests to run daily

🚀 Results

• 70% reduction in regression testing time

• 2x faster release cycle

• Increased confidence in site stability

🧩 Overview

A newly launched consumer electronics startup needed an eCommerce platform that could scale quickly with minimal technical overhead. They wanted fast performance, easy content updates, and flexibility to integrate AI features in the future.

🔍 Challenge

• Limited in-house technical team

• Expected rapid growth in traffic and SKUs

• Required a mobile-first, SEO-optimized platform

• Needed a future-ready foundation for AI personalization, chatbot support, and smart analytics

🛠️ Tech Stack Used

• Shopify Plus (backend & checkout)

• Next.js + Vercel (frontend)

• Sanity CMS (content management)

• GraphQL API

• Cloudflare CDN + Performance Monitoring

• Google Tag Manager & GA4

💡 Our Solution

We architected a headless commerce stack combining performance, flexibility, and modularity—perfect for a startup with scale in mind.

Key Actions:

• Chose Shopify Plus as the commerce engine

• Built a Next.js front-end for lightning-fast page loads and modern UX

• Integrated GraphQL APIs to decouple systems and enable future expansion

• Set up a flexible CMS (Sanity) for marketing teams to update content without developers

• Implemented basic personalization hooks using customer data (for future AI enrichment)

🚀 Results

• 80+ mobile score on Google PageSpeed

• <1.5 sec average page load time on both desktop and mobile

• Fully editable content without needing developers

• Ready to plug in AI chatbots, recommendation engines, or analytics dashboards

🧩 Overview

A fast-growing home & lifestyle brand was selling products across multiple platforms—Shopify, Amazon, and their offline POS system. They were facing frequent inventory mismatches, overselling issues, and operational inefficiencies.

🔍 Challenge

• Stock discrepancies between platforms (e.g., Amazon showed in-stock, but Shopify was out-of-stock)

• Overselling led to canceled orders and customer complaints

• Manual inventory updates were slow and error-prone

• No real-time visibility into overall stock levels

🛠️ Tech Stack Used

• Shopify Admin & Inventory APIs

• Amazon MWS / SP-API

• Custom Node.js middleware with Cron jobs

• MongoDB (for inventory logs & real-time status)

• Slack & email alerts for stock thresholds

• Power BI for demand forecasting dashboards

💡 Our Solution

We developed a custom smart inventory management system that used APIs and automation to keep stock levels synced across all channels in real-time.

Key Actions:

• Integrated Shopify, Amazon Seller Central, and Square POS using APIs

• Built a central inventory dashboard with stock alerts and reorder thresholds

• Set up AI-driven demand forecasting to predict out-of-stock risks

• Enabled bulk product updates, multi-warehouse tracking, and smart replenishment automation

🚀 Results

• 100% stock sync across platforms

• 97% reduction in overselling incidents

• Saved 8–10 hours per week in manual updates

• Increased operational confidence and customer satisfaction