An ecommerce company faced ongoing challenges processing product information received from manufacturers in PDF format. These documents often contained technical specifications in long paragraphs, complex tables, and inconsistent layouts. Before products could be listed online, internal teams had to manually review each file and extract key attributes such as size, weight, material, and other product-specific details.
This manual process was repetitive, time-consuming, and vulnerable to data entry errors, which could result in inaccurate or incomplete product listings. As SKU volume continued to grow, maintaining speed, accuracy, and consistency across product pages became increasingly difficult.
To streamline product onboarding, the company implemented the Cognitive Data Extractor (rannsCDE), an AI-powered solution designed to automate attribute extraction from manufacturer product specification documents. Using template-free parsing powered by machine learning, rannsCDE intelligently analyzed unstructured text and tables to identify relevant product attributes across diverse document formats.
The system also used a semantic tagging engine supported by product-specific dictionaries to accurately classify attributes across multiple product categories. This enabled the company to process thousands of specification sheets with minimal manual intervention while improving the consistency and completeness of product data.
rannsCDE delivered immediate measurable impact, achieving more than 90% attribute coverage and significantly improving product data quality. Product onboarding time was reduced by 70%, enabling ecommerce teams to upload more than 30,000 SKUs each month. As a result, marketplace listings moved faster, time-to-market improved, and product detail pages became more accurate and user-friendly. The solution streamlined internal workflows while giving the company a stronger competitive advantage in the online retail space.
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