Work↗03 — Featured↗E-Commerce & Generative AI

Thrift Plug.

AI-powered premium thrift e-commerce with real-time atomic inventory locking.

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CLIENT

Thrift Plug (Cameroon)

ROLE

Lead Fullstack & DevOps Engineer

YEAR

2026

DURATION

3 Months

STATUS

In Production

§01 — OVERVIEW

Thrift Plug is a custom-engineered e-commerce platform built to solve the unique operational and inventory challenges of the vintage retail market in Cameroon (serving Douala and Yaoundé). Unlike standard e-commerce sites where items have multiple quantities, thrift stores deal exclusively with one-of-a-kind products. This platform features a high-performance headless Next.js frontend integrated with Payload CMS, automated multimodal AI cataloging via Google Gemini, a custom atomic stock-locking engine to prevent double-purchasing race conditions, and a fully containerized CI/CD DevOps pipeline for continuous delivery.

§02 — CHALLENGE

Preventing Double-Selling on One-of-a-Kind Items

Since every thrifted garment is unique, a standard checkout flow creates a real risk: two customers buying the same item within the same second could both succeed, resulting in a sale the store can't fulfill.

Cataloging Bottlenecks at Volume

Uploading hundreds of unique garments weekly meant manual data entry for brand, size, color, category, and description — a bottleneck that capped how fast the business could scale, and a steady source of human error.

Image-Heavy Pages on Slow Connections

Thrift shoppers rely on high-resolution photos to judge garment condition, but serving thousands of heavy images directly was expensive and hurt load times on slower local mobile networks.

§03 — THE BUILD

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Thrift Plug homepage grid featuring premium one-of-a-kind vintage apparel

§04 — APPROACH

I designed a checkout flow with atomic, time-boxed reservations, so the moment one customer starts checkout on an item, it's locked and any concurrent attempt on the same item is safely rejected — with automatic cleanup for abandoned checkouts. For cataloging, I built an AI-assisted pipeline: newly uploaded garment photos are automatically analyzed to generate the product name, brand, color, category, and a sales-ready description, cutting a manual multi-field data-entry task down to a photo upload. Media is served through dedicated object storage and a CDN rather than the application server, keeping image delivery fast without adding database load. The whole platform ships through an automated CI/CD pipeline with integration tests gating every release, so updates go out reliably without manual deployment steps.

STACK

Next.jsMongoDBHostinger VPS

§05 — OUTCOMES

100%

Zero Double-Purchasing Errors in Production

< 5s

Multimodal AI Garment Cataloging and Analysis Time

100%

Automated DevOps CI/CD Deployment Pipeline

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