Our client, ModeLux Retail, is a global lifestyle and fashion brand operating across six countries, managing a catalog of over 100,000 SKUs and multiple regional warehouses. They faced mounting challenges:

• Outdated monolithic architecture that slowed their ability to launch new localized campaigns or respond to flash sales.
• A lack of personalized shopping experiences, leading to stagnant conversion rates and growing cart abandonment.
• Inventory inefficiencies causing frequent stockouts in one region while the same products languished unsold elsewhere.

They envisioned a future-ready e-commerce platform that would:

• Provide Amazon-grade personalized recommendations and predictive merchandising.
• Enable seamless, hyper-fast shopping experiences on mobile and desktop.
• Dynamically orchestrate multi-country inventory and fulfillment to eliminate overselling and reduce holding costs.
• Empower their marketing team to launch region-specific promotions in days, not months.

They chose ThoughtWin IT Solutions for our ability to combine robust AI, scalable cloud architecture, and deep e-commerce engineering expertise.

• Personalized Recommendations: Python Scikit-Learn pipelines drove smart recommendations, boosting AOV with “Complete the Look” and similarity models.
• Fast Catalog Search: ElasticSearch enabled <100ms faceted catalog search across 100k+ SKUs.
• Inventory Orchestration: Built multi-warehouse stock orchestrators that rerouted orders on-the-fly, preventing overselling.
• Progressive Web App: ReactJS PWA delivered app-like performance with offline caching for slow networks.
• Compliant Infrastructure: Hosted on AWS EKS with full PCI DSS & GDPR compliance layers.

• ElasticSearch vs Traditional RDB Queries: Opted for ElasticSearch to power deep faceted filters (size, style, stock) with <100ms latency, as PostgreSQL JSONB was too slow under large SKUs.
• Service Mesh for Microservices: Adopted lightweight service mesh (Envoy) so stock, recommendation, and payment services communicated securely, easily tracing bottlenecks with distributed tracing.
• Hybrid Caching Strategy: Combined Redis for ephemeral product/cart data with CDN edge caching for images, minimizing TTFB globally.
• Separate ML API Layer: Isolated recommendation models from cart flows so marketing could tweak recommendations without impacting checkout times.

Designed a mobile-first, React PWA that loads fast on weak networks and lets shoppers browse even if briefly offline. Added admin dashboards highlighting low velocity items and automatically proposed discounts (which marketing could approve in one click). Customer account pages visualize purchase histories and loyalty points with subtle animations, improving engagement.

• Dropped cart abandonment by 18% with faster, smarter checkouts.
• Improved inventory turnover by 25%, reducing warehouse holding costs.
• Cut localized campaign launch time from months to <2 weeks.