AI-Driven Application for Data Structuring and Intelligent Meeting Analysis

Abstract

The rapid growth of remote work and online learning has created a strong need for intelligent tools to record, analyze, and summarize meetings effectively.This paper presents an application DataMate, an AI-powered Virtual Meeting Summarization System designed to automatictranscription and generation of concise meeting summaries in remote and hybrid environments The system integrates Automatic Speech Recognition (ASR), and Generative AI with Large Language Models (LLMs) to extract key discussion points and attribute speaker contributions accurately. Leveraging Natural Language Processing (NLP) and Machine Learning (ML), DataMate delivers precise and efficient summaries . The application described not only helps with audio summarization but will enable the use of data analytics in the creation of content and answering questions from discussion meetings. The Application enables greater collaboration, productivity, and decision-making within an organization or learning environment by overcoming various challenges including accuracy and privacy concerns.

Introduction

The text describes DataMate, an AI-powered integrated platform designed to simplify data handling and meeting analysis by combining multiple tools into a single system.

It addresses the problem of unstructured data processing and inefficient workflows, where users currently rely on separate applications for recording, transcription, analysis, and visualization. DataMate solves this by offering a unified solution that automates these tasks using AI.

The system has two main modules:

1. Data Analysis Module:
It processes various file types (PDF, images, text, CSV) using OCR, parsing, and AI (Google Gemini). The data is cleaned, structured into tabular form, converted into CSV, and then analyzed using statistical and NLP techniques. It generates summaries, insights, and visual charts, and stores results in JSON format for quick retrieval.

2. Meeting Analysis Module:
It records or uploads audio/video meetings, converts speech to text using ASR tools like AssemblyAI, and then applies NLP and AI models to generate transcripts, summaries, key insights, and structured reports. It also identifies speakers and stores results for future use.

Overall, DataMate is designed to improve productivity, decision-making, and data accessibility by automating data structuring, visualization, and meeting intelligence in a single AI-driven application.

Conclusion

This paper showcases the capabilities of AI-powered summarization that can facilitate virtual meeting documentation using DataMate Application. The use of artificial intelligence technologies such as ASR, NLP, and LLM allows creating relevant and accurate summaries. With the help of machine learning technology, it becomes possible to collect key points without information overload. However, there are still problems such as attribution of speakers, overlap of dialogues, confidentiality, and domain adaptation. In future, attention will be paid to multimodal learning, live summarization, and ethical AI technologies. Furthermore, in future, there may be added support of multiple languages, AI chat assistance, more efficient voice recording, and performance on any device.

References

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Copyright

Copyright © 2026 Vijaya Balpande, Shivani Duble, Pranali Barapatre, Pranali Khapne, Trupti Lakhe, Shital Ghivtonde. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.