Data reconciliation is a foundational requirement across analytical pipelines, regulatory reporting, machine learning workflows, and enterprise data engineering. Modern systems generate large volumes of heterogeneous data that undergo complex transformations across distributed environments. These transformations introduce discrepancies, semantic drift, and structural inconsistencies that are difficult to detect, explain, and resolve. Traditional reconciliation tools rely on rigid rules, manual inspection, and domain-specific logic, making them brittle, slow, and difficult to scale.This paper introduces the Smart Reconciliation System, a unified architecture that performs domain-agnostic reconciliation across multiple input and output datasets, applies semantic normalization using natural language processing, identifies discrepancies, summarizes findings, and enables real-time interactive exploration through a conversational interface. The system combines a batch-oriented reconciliation backbone with a real-time agentic orchestration layer that interprets natural language queries, generates analytical plans, and produces visual explanations. It incorporates probabilistic matching, embedding-based entity normalization, constraint validation, anomaly detection, and continual learning from user feedback. The architecture includes a typed intermediate representation for reconciliation logic, optimization strategies for large-scale execution, and a roadmap for production deployment.The Smart Reconciliation System provides a portable, extensible, and semantically precise approach to reconciling data across diverse environments. It enables organizations to detect issues earlier, reduce manual effort, and interact with reconciliation results through natural language while preserving correctness, auditability, and reproducibility.
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