A Modular IPA Framework for Semantic Retrieval and Generation in Legal-Administrative Workflows - C. e Silva, Arthur; Costa, Robson; Coutinho, Daniel; Oliveira, Luis; Parente, Paulo; Caminha, Carlos (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), 2026)
The growth of document collections has made manual analysis inefficient in contexts that require synthesis and traceability. This work proposes a framework for orchestrating pipelines in the context of Intelligent Process Automation (IPA), focused on processing unstructured data. The solution integrates OCR, NLP, machine learning, and LLMs within a modular and general-purpose architecture, with support for open-source models. A case study involving more than 14,000 legal-administrative documents demonstrated that the approach enables extraction, semantic enrichment, and vector search. The results indicate gains in efficiency, organization, and support for automated document generation.