SavantShopper

Fashion e-commerce faces high customer acquisition costs. The client needed a way to attract organic traffic at scale without exposing their proprietary styling algorithms to competitors.

Built a "content moat" architecture that generates high-value public content for SEO while keeping the core AI styling engine behind a secure, invite-only gate.

1. 01 Developed hybrid rendering architecture (SSG + CSR)
2. 02 Implemented advanced bot detection and rate limiting
3. 03 Created automated content pipelines for trend analysis
4. 04 Built secure, scalable user authentication system

The core of SavantShopper is a custom-trained TensorFlow model that analyzes fashion trends from social media signals. This model feeds into a Next.js frontend that uses Incremental Static Regeneration (ISR) to create thousands of landing pages for trending items. We implemented a custom middleware to detect and block scraper bots (like GPTBot and CCBot) to prevent competitors from training on our curated datasets, ensuring the "Savant" algorithm remains a unique competitive advantage.

We utilized a "Programmatic SEO" approach, generating unique, high-value landing pages for long-tail fashion queries (e.g., "best vintage leather jacket for petite women"). By structuring this data with rich Schema.org markup (Product, ImageObject) and ensuring internal linking clusters around core style categories, we achieved a domain authority spike and captured featured snippets for over 400 keywords.