International Journal on Science and Technology

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AutoDashAI: A Research on AI-Driven Automation in Data Cleaning, Visualization, and Dashboard Generation

Author(s) Ms. Avani Sagar Dange, Ms. Ashika Jain, Ms. Haritakshi Trivedi, Prof. Vidya Sagvekar
Country India
Abstract The rapid growth of data across industries has increased the need for intelligent analytics solutions that simplify data processing and reduce reliance on technical expertise. Traditional Business Intelligence (BI) tools often require significant manual effort for data preparation, visualization, and interpretation, making them less accessible to non-technical users. Recent advancements in Large Language Models (LLMs), Agentic AI, and Natural Language Processing (NLP) have enabled the development of automated systems capable of transforming raw data into meaningful insights through natural language interaction.

This paper presents AutoDashAI, an AI-driven, no-code analytics platform designed to automate the complete analytics lifecycle, including data ingestion, extraction, cleaning, visualization, dashboard generation, and multilingual insight narration. The proposed system employs a modular agent-based architecture that supports multiple data formats such as CSV, Excel, PDF, Word documents, and images. Through intelligent prompt interpretation and automated visualization selection, AutoDashAI enables users to generate interactive dashboards and analytical insights with minimal human intervention.

Experimental evaluation demonstrates the effectiveness of the proposed approach, achieving a Data Ingestion Success Rate (DISR) of 85% and a Cleaning Efficiency Improvement (CEI) of 95%, while maintaining reliable performance through a hybrid architecture that combines LLM-based reasoning with deterministic fallback mechanisms. The results highlight the potential of agentic AI to improve accessibility, scalability, and efficiency in data analytics. AutoDashAI represents a step toward user-centric and automated analytics systems that empower informed decision-making across diverse application domains.
Keywords Agentic AI, Automated Data Analytics, Data Cleaning, Data Visualization, Dashboard Generation, Large Language Models (LLMs), Natural Language Processing (NLP), Business Intelligence, Multilingual Analytics, Explainable AI.
Field Sociology > Data / Information / Statistics
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-06-07
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