A Note on What This Is
Client names from enterprise engagements are withheld under NDA — that work was delivered under employment at an agency, and the clients are not ZaidanLab's to claim. What follows describes a real pattern of work across engagements spanning fast-fashion retail, cosmetics and skincare, luxury retail, home-improvement and consumer electronics across Southeast Asia, without attaching it to a specific brand.
The Problem Pattern
On a large Adobe Commerce or Magento catalog, generic category browsing and static "related products" blocks leave conversion on the table. The fix that actually moves numbers is rarely a bigger AI model — it's getting personalization signals (browse history, purchase history, catalog attributes) correctly piped into the storefront without breaking cache, since ecommerce personalization and full-page caching are natively in tension.
Where AI Fits
AI-driven recommendation and personalization logic sits alongside — not instead of — the caching and infrastructure layer (Redis, Varnish) that keeps an enterprise storefront fast. Getting this right is an integration problem as much as a modeling problem: PIM data (Akeneo) has to be clean enough to feed a recommendation engine, and the storefront has to serve personalized content without falling back to a full page miss on every request.
Why This Matters for Uptime, Not Just Conversion
This is the same infrastructure discipline behind taking a platform's production uptime from 93% to 99.9%: personalization and AI features are only worth shipping if they don't degrade the reliability of the platform underneath them. That trade-off — feature richness versus production stability — is where most in-house AI personalization projects actually stall.