A scalable LLM-powered system that automates product content translation and marketplace listing generation for 100k+ SKUs across multiple platforms, achieving 96% auto-mapping accuracy and significant reduction in manual localization effort.
A fast-growing European e-commerce company needed to publish 100k+ SKUs across multiple marketplaces, each with unique listing templates, attribute requirements, and tone guidelines. Their product data existed in proprietary databases with inconsistent coverage—some SKUs lacked descriptions, others had missing attributes.
The goal: build a Python/Django system that could automatically ingest CSV data, normalize it to a canonical schema, and generate platform-compliant listings in multiple languages with image-aware content enrichment.
The system achieved remarkable efficiency gains:
Building a multilingual, multi-marketplace content pipeline involved complex technical and business challenges:
We engineered a comprehensive Python/Django system with OpenAI integration that transforms messy product data into high-quality, localized marketplace listings through intelligent automation and validation.
CSV upload with dual-engine header mapping: embeddings-based similarity matching and symbolic rules for semantic understanding. Active learning loop where human corrections improve accuracy over time. Pydantic validation ensures data quality from ingestion.
OpenAI vision models extract product attributes from images while text models generate localized content. Structured JSON outputs via function calling ensure reliable data contracts. Image-aware descriptions provide richer, more compelling copy.
Canonical product schema transforms to platform-specific formats (Amazon, Shopify, Zalando, bol.com). Constraint-aware generation respects field lengths, mandatory attributes, and marketplace policies. Automated compliance linting prevents rejections.
Pydantic models enforce strict validation contracts. Human-in-the-loop review for low-confidence cases. Audit trails track every generation with prompt versioning and content hashing for full observability.
If you're looking for scalable SaaS design, deep integration with complex APIs, or predictive tooling for real-world operations—this project is a proven case study of robust, end-to-end execution.