Cleansera: A Context-Aware, Algorithm-Centric Data Cleaning System with RAG-Enhanced Intelligence

The two most difficult tasks of data analytics are usually data preparation and data cleaning; according to the research presented in this article, these activities account for approximately 50 to 80 percent of total time spent on real-world data analytic initiatives [1]. Most existing data cleaning tools use static, rule-based approaches and do not accommodate the unique needs of a specific domain, nor do they provide clear visibility of the internal ways in which they arrive at their final data-cleaned outputs [1]. This study introduces Cleansera, a context-aware, AI based data cleaning solution that places a greater emphasis on algorithmic characterization and uses flowchart-driven methodologies to achieve execution. Cleansera offers automated contextdetecting capability, Retrieval Augmented Generation (RAG), deterministic data cleaning workflows, version-controlled user interaction, and dual-checkpoint data cleaning QA capabilities [1][2]. The primary focus of this article is on the latest designed algorithms and validated workflows created during partial implementation of the Cleansera system. Through the creation of defined algorithms, execution paths, and verification checkpoints, Cleansera offers transparency, auditability, and repeatability for automated data cleaning [1]. Cleansera combines the elements of AI-driven flexibility with the principles of traditional algorithms to create a data cleaning methodology that may be adopted in both industry and academia [1][2].