A Secure On-Premises ETL Pipeline for Enterprise Data Warehousing: Integrating OCR and Local LLMs
P Shrikaran, H Mamalaivasan, D. Chitradevi, Tharun Kumar S, Abishekwoolridge · 2025
Traditional data extraction tools generally do not efficiently process unstructured enterprise data, causing valuable information in handwritten documents, forms, and scanned papers to remain inaccessible. This paper introduces a safe, on-premises Extract-Transform-Load (ETL) pipeline that combines Optical Character Recognition (OCR) with locally installed Large Language Models (LLMs) to make smart data transformation possible. Our system has used OCR modules to pull raw text out of various document structures, which is subsequently analyzed by an LLM hosted on the Ollama platform. The LLM does semantic analysis, contextual mapping, and domain-specific structuring of extracted data prior to loading into structured forms.Unlike cloud solutions, this approach emphasizes data privacy and regulatory compliance (such as GDPR and HIPAA conditions) without sacrificing low-latency processing - making it especially well-suited to organizations working with sensitive or proprietary data. We explain the system's design, optimization strategies, and include performance benchmarks from actual implementations. The findings show substantial improvements in data extraction and transformation performance, leading ultimately to improved business intelligence and decision processes. Our scalable solution is especially effective in enterprise usage in which data security and processing efficiency are of the utmost importance.