Engineering scope
My applied AI work at Veeva spans LLM and RAG workflows for document processing, structured extraction, metadata enrichment, vector search, and enterprise search. It also includes agentic workflows over governed enterprise data.
This extends my platform engineering scope into the unstructured information used in business workflows.
From documents to structured data
Document processing and structured extraction turn information in text into fields that can be modeled and used downstream. My scope includes the LLM workflows and metadata enrichment around this work.
The public overview describes the responsibility. It does not disclose document sources, prompts, extraction schemas, evaluation results, or internal business workflows.
Retrieval and governed access
RAG, embeddings, vector search, and enterprise search are part of my technical work. My public writing explores the relationship between those capabilities and trusted data, business context, permissions, and operating cost.
Read my published perspective on applied AI for the engineering questions I focus on.
What is established
My resume and LinkedIn profile establish this architecture scope. No model vendor, production adoption figure, accuracy result, cost saving, or autonomous action outcome is claimed in this overview.