Example build · D2C fine jewelry
From a blocked SaaS store to an AI-native commerce platform
A fine jewelry brand selling D2C and on marketplaces. Their SaaS platform blocked campaigns, content work ate two days a week and the marketplace launch kept slipping. We delivered the full BEAM cycle: Blueprint, a fixed scope build, AI automation and ongoing growth.
- Migration from SaaS
- AI Content Engine
- Kaufland feed
- Maintenance & Growth
From workshop to launch
9 weeks
Manual work removed
34 h / month
Conversion after relaunch
+18%
Sales channels at launch
3
The storefront
A store that feels like a lookbook and converts like a landing page
A custom, fast storefront: full-bleed campaign imagery, collection edits and a product grid with quick add, all editable by the team without a developer. Product data was cleaned during migration, so filters, materials and stone attributes finally work.
An example build: it shows the scope, pace and delivery model of a typical BEAM engagement. The brand shown is illustrative, hence the blurred logo.
The admin
One panel to run the whole operation
Orders, products and inventory in the platform’s standard admin (Medusa), plus Seedlight modules for AI automation, analytics and marketplace feeds. No juggling five tools.
01
AI Workflows
Product descriptions, PL/DE/EN translations, feed localization and listing error analysis run as workflows inside the admin. Each one shows its trigger, run history and time saved, and can be paused with one switch. 34 hours of manual work removed every month.
02
Analytics
Revenue, orders, conversion and channel split in one view, next to the AI impact panel: how many descriptions were generated and how many feed errors were fixed automatically. The monthly growth priorities come straight from this screen.
Stack
- Medusa.js 2.0
- Next.js storefront
- PostgreSQL
- Seedlight AI Content Engine
- Feed & Listing Engine
- Cloudflare
Launched in week 9. Growing ever since.
After launch the platform moved into Maintenance & Growth: monitoring, monthly priorities and Feature Sprints. Kaufland went live with the same product data, translated and validated automatically.
