AI automation speeds product ops for ecommerce catalog
Deployed n8n pipelines and model evaluations to enrich SKUs, generate descriptions, and triage support for an auto parts retailer.
The results
time to publish
return rate
faster first response
Challenge
Our ecommerce client manages a catalog of 90,000 SKUs with inconsistent attributes and long publishing cycles. Content and support teams were stretched, and return rates were rising due to mismatched fitment info.
Approach
We designed an AI workflow that pulled supplier feeds, normalized attributes, and generated first draft descriptions with fitment checks. We added evaluation steps to catch hallucinations and enforced human in the loop approvals. In support, we deployed a triage copilot that classifies issues and drafts responses with references to order data.
Build
Implemented n8n pipelines, Dockerized services for model calls, and a monitoring layer that logs prompts, outputs, and evaluation scores. We integrated the pipeline with the PIM and ticketing system and added guardrails for PII.
Impact
Time to publish new product pages dropped by 45 percent. Clearer specs reduced returns by 23 percent, improving gross margin. Support CSAT climbed 38 percent as first response times fell and answers included verified links.
Stack
n8n, Docker, OpenAI and local models, Pinecone for retrieval, PIM, Zendesk, monitoring dashboard.