Abstract

Context-aware Robotic Process Automation (RPA) represents a significant advancement beyond traditional rule-based automation by addressing the challenges of unstructured data processing. Integrating Natural Language Understanding (NLU) capabilities with RPA frameworks enables intelligent automation across diverse scenarios involving free-text communication, variable document formats, and conversational inputs. Transformer-based language models enable the extraction of intent, entities, and contextual relationships from emails, chat transcripts, and reports, facilitating autonomous interpretation and action on unstructured inputs. The architectural framework encompasses a language processing layer, a semantic action mapper, and a confidence-based escalation mechanism for handling ambiguity. Implementation in customer support ticket triage demonstrates effective categorization of requests, extraction of relevant information, and appropriate routing with minimal human oversight. This integration extends automation capabilities into domains previously inaccessible due to contextual understanding requirements. The practical applications span multiple industries, including healthcare documentation, financial compliance, and customer service operations. These advancements signal a paradigm shift in automation technology that bridges the gap between structured process execution and human-like comprehension of unstructured content.

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