We began by analyzing sample documents alongside brand compliance stakeholders, quickly proving that traditional data extraction failed on image-heavy PDFs. In alignment with the client, we pivoted to a vision-enabled Large Language Model (LLM) capable of “reading” flat images. We built a scalable extraction pipeline utilizing the client’s most cost-effective, vision-capable model to reliably pull the required brand data. Crucially, we engineered the system architecture with a highly generic prompt logic. This embedded seamlessly into the auditing workflow, ensuring teams can instantly adapt the tool to entirely new document compliance use cases by simply changing the target configuration without altering the underlying application code.
