Case Study/ ContentOps
A global supplier of industrial and electronic products and services needed a scalable solution to automate the enrichment of 150k product attributes weekly from 15k products spread across 30k documents. The data, which was spread across diverse formats like PDFs and webpages, required manual-intensive processing and compromising data quality.
Managing product data at scale was challenging due to the need to source assets for numerous products and process large volumes of documents in different formats. Manual attribute extraction slowed operations, while scattered information across datasheets and product pages made consistency difficult. Ensuring data compliance while enabling automation added to the complexity.
Automating the process of downloading the documents relevant for the products.
Unstructured data from documents gets segmented into manageable chunks stored in a vector database.
Extracts and update attributes, manufacturer details, and metadata and save into a database.
Missing attributes updated from the open web using AI-driven workflows.
Custom rules for precise enrichment.
Quality control workflows maintain data reliability.
