Example build · Multibrand retail
12,000 SKU described by workflows, approved by people
A multibrand retailer with 12,000 SKU and three markets. The content team could not keep up: new products launched without descriptions, translations waited weeks. We did not rebuild their platform, we wired AI workflows into it: descriptions, translations and feed localization with a human review step.
- Existing platform
- AI Content Engine
- 3 languages
- Priced per workflow
SKU covered in 3 languages
12,000
Manual work removed
58 h / month
Content published with human review
100%
Workflows in production
6
The output
Content the customer actually sees
The German market page of one of 12,000 SKU: a complete description, full attributes and localized details, generated by workflows, approved by the content team, live in three languages. This is what the hours saved buy: product pages that sell instead of empty templates.
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 rollout
Started with one workflow, scaled on numbers
The pilot was product descriptions for one category. When the numbers held, hours saved, quality accepted by the content team, the next workflows followed. Priced per workflow, added only where they pay back.
01
AI Workflows
Six workflows run inside the admin: descriptions, translations into two languages, feed localization, attribute mapping and listing error analysis. Each one shows its trigger, run history and hours saved, and every result waits for human approval before it goes live.
02
Analytics
The AI impact panel shows generated descriptions, translation coverage and hours saved per workflow, next to revenue and orders. The decision about each next workflow is made on this data, not on enthusiasm.
03
Marketplace Feeds
Localized content flows straight into channel feeds: descriptions and attributes per marketplace requirements, validated before publication. Content and channels stopped being separate projects.
Stack
- Seedlight AI Content Engine
- Integration with the existing platform
- Human review gates
- Feed & Listing Engine
- Cloudflare
58 hours back every month. Content stopped being the bottleneck.
New products go live with descriptions in three languages on day one. The content team reviews instead of writing, and the sixth workflow, support reply suggestions, is in testing.
