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Fri, April 24 at 11:00 AM - 11:30 AM GMT+5:30DataTech Architecture
Many organizations did not truly transform their document workflows. They moved paper into digital files and carried the same workflow problems forward. As documents progressed from physical records to digital artifacts and now to automated workflow events, the real change is not about removing paper. It is about treating documents as structured data that moves through reliable, intelligent pipelines.
This session examines how document workflows have evolved and why simple digitization failed to solve core process issues. It explains how modern systems ingest documents, extract and validate information, and route data through automated workflows. The session also looks at the role of AI and intelligent document processing in converting documents into actionable data that can be used reliably across systems.
What You Will Learn
How document workflows evolved from paper-based processes to automated pipelines
Why digitizing documents alone did not fix workflow and integration problems
How modern systems extract, validate, and route document data using automation and AI
Who Should Attend
Developers
Software and enterprise architects
Product managers
Teams building or modernizing document-heavy systems
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Tate Andrea Aung is a Solutions Engineer at Apryse, specializing in helping corporate and mid-market enterprises across the APAC region transform manual, document-heavy processes into highly automated, secure data pipelines. Her work spans the full document lifecycle – from document rendering and smart data extraction to intelligent annotation, redaction, and legally compliant digital signatures – helping organizations in regulated industries translate complex technical requirements into working integrations.
Having spent three years in the field at Apryse, Tate is knowledgeable about designing document automation architectures that are production-ready, developer-friendly, and built to scale – with particular focus on on-premise pre-processing patterns that hold up under data compliance constraints.
