International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
Cleansera: An Intelligent Desktop Application for Domain-Specific Data Cleaning Shubham Deshmukh, Om Bhise, Shubham Rao, Vishal Daware 1,2,3,4Student, Dept. of Information Technology, MET's Institute of Engineering, Nashik, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------professionals have to set up the validation rules manually, Abstract – We know in today’s world data professionals
a process that takes time and can easily lead to mistakes. These tools lack intelligence to understand the context of the data for example a “date” field in a financial ledger is treated identically to a “date” field in a supply chain manifest. This becomes even more difficult for professionals who handle data across different industries. Each domain whether it’s Banking, Financial Services, and Insurance or supply chain management has their own rules and standards that needs to be followed during data preparation.
spend so much of their time, almost 50-80% on manual cleaning, this blocks the path to analysis. This paper presents Cleansera, an AI-powered desktop application made to speed up this process. Cleansera introduces a context-aware cleaning approach, this enables the system to apply industry-specific standards for domains like finance, supply chain, manufacturing. A RetrievalAugmented Generation (RAG) framework is used in the system, allows users to interact with their data using natural language commands. To ensure data integrity, the system uses a dual-checkpoint quality assurance mechanism which tracks data loss and master fields. Application is built using Electron.js, functioning completely local with data privacy are the prioritizes. Ensuring high accuracy in AI command clarification are the key objectives. Cleansera automates complex data cleaning, allowing data professionals to focus on analysis and make accurate data-driven decisions.
To overcome these inefficiencies, this study introduces Cleansera, an innovative desktop application that performs context-aware data cleaning. Cleansera is an AIpowered tool that understands and adapts to specific industry contexts by applying domain-specific data cleaning strategies. The system uses a RetrievalAugmented Generation (RAG) framework, this allows the system to access external industry knowledge bases and best practice resources. This makes sure that all cleaning processes follow the right industry standards, moving past generic rules to create a smarter and more efficient data preparation solution.
Keywords: Data Cleaning, Context-Aware Systems,
Retrieval-Augmented Generation (RAG), Artificial Intelligence (AI), Data Preparation Automation, DualCheckpoint Quality Assurance, Domain-Specific Data Standards, Electron.js Application, AI Systems, Natural Language Interface, Knowledge Retrieval, Data Integrity Verification, Industry-Aware Rule Application, Data Loss Detection, Cross-Platform Desktop Application
2. LITERATURE REVIEW The automation of data cleaning using large language models (LLMs) and artificial intelligence (AI) has been studied by several researchers in recent years. However, scalability, domain adaptation, transparency and real-time quality verification remain major obstacles for most current systems.
1.INTRODUCTION In today’s data-centric world, information is a key asset for decision-making and organizational strategy. To understand customer behavior, optimize operations, and plan for future growth, many businesses, small or large, rely on high quality data. However, the raw data collected by organizations is rarely ready for analysis. The data cleaning and preparation are essential yet complex processes, due to different quality standards across different industries. For example, the rules for financial transaction are different from those required for marketing or ecommerce.
The effectiveness and scalability of traditional data cleaning and preprocessing tools were evaluated by Pedro Martins et al. In 2025. Their research showed that although these tools work well on structured datasets, they do not take domain-specific validation and contextual understanding into account. To get around this limitation, Cleansera offers context-aware intelligence that modifies intelligence that modifies cleaning processes based on supply chain, manufacturing and BFSI Domains.
Traditional data cleaning tool often can’t handle this complexity, as they rely on one-size-fits-all approach. Due to this approach the domain-specific requirements, compliance requirements, and standards that each industry sector demands are ignored. Because of this, data
© 2025, IRJET
|
Impact Factor value: 8.315
AutoDCWorkflow, an LLM-based system that automatically creates data cleaning workflows, was presented by Lan Li et al. In 2025. The system needed human validation for most of its decision and lacked
|
ISO 9001:2008 Certified Journal
|
Page 790