Case study / Delivery record
Generative AI inside the PIM: product content at scale
I scoped and led a production generative-AI feature inside Pimcore for a high-volume product catalogue. Human review, configurable prompts and measured hours saved made the feature useful without exposing customers to unverified output.
- Pimcore
- OpenAI / Claude
- per-field product-content generation
- human review before every save
- admin-configurable field prompts
01
Context
Catalogue volume had outgrown manual product-content creation.
A client’s product catalogue needed meta descriptions and field content at a volume the merchandising team could not write by hand.
02
Challenge
AI had to save time without publishing unverified answers.
Put generative AI where it earns its keep, behind the glass, with human review, rather than on the storefront where a wrong answer costs a customer.
03
What I did
Human review and configurable prompts made generation governable.
Scoped and delivered an AI content-generation feature inside Pimcore with per-field generate buttons, a review pop-up before save, admin-configurable field and prompt lists and strict character-limit enforcement; ran the model selection between OpenAI and Claude with the client; measured AI-assisted productivity with an estimated-vs-actual hours analysis across the team.
04
Outcome
The production feature made AI-assisted effort measurable.
Production feature in the client’s PIM; a defensible measure of where AI saves hours and where it does not.
05
What it taught me
Put AI where a wrong answer costs a forecast miss, not a customer.
Put AI where a wrong answer costs a forecast miss, not where it costs a customer.
- 01
Use-case scoping
- 02
Model selection
- 03
Pimcore build
- 04
Human review
- 05
Productivity study