NexaShop E-Commerce Platform

NexaShop approached us with an ambitious brief: build a marketplace capable of onboarding thousands of vendors within the first quarter, each with their own product catalogue, pricing rules, and payout schedules. The existing Shopify-based storefront had hit its ceiling — custom vendor logic required workarounds that were slowing the development team, and page load times were creeping past 5 seconds on mobile. Beyond performance, the business model demanded complex financial orchestration: split payments, delayed vendor settlements, refund workflows across multiple sellers in a single cart, and fraud screening on every transaction. Compounding this was an international audience spread across 14 countries, each expecting local currency pricing, regional tax compliance, and native payment methods. The technical challenge was therefore threefold — build a platform that was architecturally clean enough for the in-house team to maintain, scalable enough to handle flash-sale traffic spikes of 10× baseline, and flexible enough to plug into third-party logistics, ERP, and accounting systems without bespoke integrations for each.

Our solution was a purpose-built, headless commerce architecture anchored on Next.js 14 server components. By moving product pages, category listings, and search results to server-side rendering with Incremental Static Regeneration (ISR), we achieved sub-2s First Contentful Paint globally — without sacrificing the dynamic personalisation the business needed. The backend was decomposed into four focused microservices: a product catalogue service (Node.js + PostgreSQL), an order management service, a vendor portal API, and a notification service. Each service communicated via a lightweight message bus, allowing independent deployment and zero-downtime updates. Redis served as the caching layer across all services, reducing database read load by 68% during peak periods. For payments, we integrated Stripe Connect with a custom multi-party payout engine. Vendors receive automated settlements on a configurable schedule (weekly or bi-weekly), with real-time dashboards showing pending payouts, dispute status, and earnings history. AWS CloudFront with Lambda@Edge handled asset delivery and geo-routing, ensuring product images and CSS bundles loaded in under 200ms regardless of visitor location. The AI recommendation engine was built using collaborative filtering and content-based signals, trained on historical purchase data and updated nightly. It surfaces personalised product carousels on the homepage, cart cross-sells, and post-purchase upsells — directly contributing to the 43% uplift in conversion rate observed in the first 90 days post-launch.