Efficient translation of HTML to JSON for enhanced web content production

The automated transformation of unstructured HTML into schema-compliant data is a foundational challenge for platform interoperability and the scalability of modern no-code web editors. While powerful, Large Language Models (LLMs) are often ill-suited for this task due to their inherent stochasticity and computational cost, failing to guarantee the deterministic precision required at scale. This paper addresses this challenge by introducing a novel, deterministic pipeline to translate arbitrary HTML emails into the proprietary, grid-based JSON of the Beefree content platform. Our core contributions are: (1) a hybrid methodology that combines Document Object Model (DOM) analysis for semantics with computer vision for geometric layout interpretation; (2) a vision-based abstraction technique using visual placeholders for robust row-column detection, resilient to DOM structural variations; and (3) a rigorous, dual-faceted validation of its real-world viability via a large-scale assessment on over 16,000 HTML emails and qualitative usability studies (SUS) with 16 industry professionals and 10 academic researchers. The results confirm our deterministic, vision-augmented approach is a highly effective and scalable alternative to generative models for structured content creation in production environments.