AI Digital Twin (SelfX.ai): Intelligent Communication Assistant

AI Digital Twin (SelfX.ai): Intelligent Communication Assistant

Under the guidance of

Dr. Varsha Jadhav

Amogh Joshi, Manojkumar Kulkarni, Samridhi Pal, Surabhi Biradar

Department of Information Science and Engineering SDMCET

Abstract – There is rapid increase in digital communication repeatedly. Processing and AI. The system generates personalized replies,

that created challenges in managing emails, messages and calls The systems we have now can do some things automatically efficiently. This paper presents an AI Digital Twin (SelfX.ai), an but they do not really understand what is going on what is important intelligent system that learns the way user communication and how each person likes to communicate. Also there is no one behavior and automates responses using Natural Language place that brings together messages, emails and calls into a system. prioritizes communication, and handles calls using Twilio-based

voice assistance. The proposed solution improves productivity,

tasks easier for the LLM to process, and provides a Enhanced The main problems are:

communication automation framework.

Index TermsAI Digital Twin, NLP, Communication Automa- We get many notifications and it is hard to know what to look at

tion, Twilio, Smart Assistant We do not have a personal assistant to help us with communication

1. Introduction

   Digital communication is a part of our lives now. We are We do not handle calls well when we are really busy

   always talking to each other through messaging platforms, We do not have one system to manage all our communication, emails and phone calls. This has made it easier for us to like digital communication digital communication is a big part of connect with each other.. It has also made things a bit crazy. this issue and digital communication systems should be improved to We get many messages and it is hard to keep up. This can be solve these problems.

   really difficult. It makes user to get the things harder.

   We also get interrupted a lot. We have to deal with too many notifications. Sometimes we do not even get a response when we need one. This is not good for our work or our mental health. The systems we use now to communicate are not very helpful. They do not do much to make things easier for us. They are not very personalized.

   They do not know how we like to communicate or what we like to say. This is a problem because we all communicate in ways. We need a system that can understand how we communicate and help us out. The AI Digital Twin, which is also called SelfX.ai is trying to solve this problem.

   It makes a copy of us that learns how we communicate over time. It uses tools like Natural Language Processing and machine learning to make this happen. It leverages cloud-based services to automate things like email and messaging responses. This makes things a lot easier for us. It helps us to get things done more quickly. The AI Digital Twin is, like an assistant that really understands us and helps us to communicate in a better way.

2. Problem Statement

   The rapid increase in digital communication has led to several challenges that affect both individuals and organizations. The way we communicate digitally is changing fast and this is causing a lot of problems, for people and organizations. Every day people get a number of messages, emails and calls and it is

3. Objectives

   • To develop a personalized AI Digital Twin for commu-nication

   • To implement intelligent SMS and email reply generation

   • To integrate AI-based call handling using Twilio

   • To provide a centralized platform for communication management

   • To improve user productivity and reduce cognitive load

4. Literature Survey

   Recent advancements in Artificial Intelligence and Natural Language Processing have enabled the development of intelli-gent virtual assistants and automated communication systems. Several studies have explored the use of NLP techniques for intent detection, sentiment analysis, and response generation in chatbots and conversational agents.

   Digital twin technology, originally used in industrial appli-cations, has evolved to include personalized digital representa-tions of users. These systems aim to replicate human behavior and decision-making processes. However, the application of digital twins in personal communication management is still in its early stages.

   Existing systems such as virtual assistants provide limited personalization and lack integration across multiple commu-nication platforms. Email automation tools offer basic

   hard to figure out what to answer first and how to answer it. A lot categorization but do not generate context-aware responses. of things get missed because we get too many notifications and

   we spend a lot of time doing the same communication tasks

5. SYSTEM ARCHITECTURE intent, sentiment, and urgency. This helps in classifying mes-

   The AI Digital Twin system follows a multi-layered client-server architecture designed for scalability, modularity, and efficient data processing.

   The architecture consists of the following layers:

   Client Layer: The Android application serves as the user interface, allowing users to interact with the system. It in-cludes features such as message viewing, reply approval, Busy mode toggle, and call summary display. Backend Layer: The backend processes data, performs AI-based computations, and manages communication between components. It handles au-thentication, message classification, and response generation. Database Layer: Firebase Firestore is used to store user data, communication logs, and feedback information, enabling real-time synchronization. External Services: APIs such as Gmail API, Twilio, and OpenAI are integrated to provide email access, call handling, and AI capabilities.

   This layered architecture ensures efficient communication,

   sages and prioritizing communication. AI Response Genera-tion: AI models generate context-aware replies based on user writing style. Multiple response options are provided for user approval. Call Handling: Twilio is used to manage incoming calls. When Busy mode is enabled, the AI agent responds to calls with predefined messages and collects caller information. Feedback Learning: User feedback is used to continuously improve the AI model, enabling better personalization over time.

   1. Data Collection

      User messages and emails are collected to understand communication patterns.

   2. NLP Processing

      Messages are analyzed for intent, sentiment, and urgency.

   3. AI Response Generation

      scalability, and secure data handling. AI models generate personalized replies based on user style.

      • Client Layer: Android application interface

      • Backend Layer: AI processing and data management

      • External APIs: Gmail API, Twilio, OpenAI

        Fig. 1. System Architecture

   4. Call Handling

   Twilio routes incoming calls to the AI agent, which responds using predefined messages.

   Fig. 2. Sequence Diagram

   1. MODULES

6. METHODOLOGY The system is divided into several functional modules:

The system operates through a structured workflow that includes data collection, processing, response generation, and continuous learning.

Call Assistance Module: Handles incoming calls using Twilio and provides automated voice responses. It ensures that important calls are not missed during busy periods. Mes-

Data Collection: User communication data such as messages sage Processing Module: Analyzes SMS messages using NLP and emails are collected and stored securely. This data is used techniques and classifies them based on urgency and content. to analyze user behavior and preferences. Natural Language Email Automation Module: Processes emails using Gmail API Processing: NLP techniques are applied to analyze text data for and generates AI-based reply suggestions. AI Trainer Module:

Learns user communication behavior and updates the model to improve response accuracy.

1. Call Assistance Module

   Handles incoming calls and provides automated voice re-sponses.

2. Message Processing Module

   Classifies and prioritizes messages.

3. Email Automation Module

   Processes emails and generates replies.

4. AI Trainer Module

Learns user behavior and improves response accuracy.

1. DESIGN

   1. ER Diagram

      Represents relationships between users, messages, and re-sponses.

      Fig. 4. Class Diagram

      Fig. 3. ER Diagram

      X. Results and Discussion

      The system was tested under various scenarios to evaluate its performance and effectiveness. The results indicate that the AI Digital Twin successfully automates communication tasks and provides personalized responses.

      The message classification module accurately categorizes messages based on urgency, while the AI response generation module produces context-aware replies. The call assistance module effectively handles incoming calls, ensuring commu-nication continuity.

      The system demonstrates improved productivity and re-duced communication overload. However, performance may vary depending on network conditions and API response times.

      XI. Future Scope

      • Autonomous AI decision-making

      • Integration with social media platforms

      • Context-aware communication assistance

   2. Class Diagram XII. Conclusion

   Defines system structure and object relationships. The AI Digital Twin (SelfX.ai) presents an innovative

2. Implementation

The system is implemented using modern technologies to ensure efficiency and scalability. The Android application is developed using Kotlin and Jetpack Compose, providing a responsive and user-friendly interface.

Firebase Authentication is used for secure user login, while Firestore is used for data storage and real-time synchro-nization. Twilio API enables voice-based call handling, and OpenAI API provides AI capabilities for response generation. The integration of these technologies ensures seamless communication between system components and efficient ex-

ecution of functionalities.

solution for managing digital communication using artificial intelligence. By automating repetitive tasks and providing personalized responses, the system improves efficiency and user experience.

The integration of NLP, machine learning, and cloud ser-vices demonstrates the potential of AI-driven automation in real-world applications. The project serves as a foundation for future advancements in intelligent communication systems..

References

1. K. Govindharaju et al., AI-Twin: AI-Powered Digital Twin for Personalized Assistance and Predictive Decision-Making, 2025. [Online]. Available: https://rsisinternational.org/journals/ijrsi/digital-library/volume-12-issue-3/609-617.pdf

2. V. M. and S. G. V., The Use of Natural Language Processing in Virtual Assistants and Chatbots, 2022. [Online]. Available: https://www.ijert.org/the-use-of-natural-language-processing-in-virtual-assistants-and-chatbots

3. A. Yadav et al., AI-Powered E-Mail Assistant Using Spring Boot, 2025. [Online]. Available: https://www.irjmets.com/upload_newfiles/irjmets70900 047621/paper_file/irjmets70900047621.pdf

4. S. Sidharthan and A. C. V., A Personal Virtual AI Assistant, 2025. [Online]. Available: https://www.ijrti.org/papers/IJRTI2503207.pdf

5. J. Alfaro-Viquez et al., A Comprehensive Review of AI-Based Digital Twin Applications, 2025. [Online]. Available: https://www.mdpi.com/2079-9292/14/4/646

6. A. Bastola et al., LLM-Based Smart Reply: Enhancing Collaborative Performance, 2023. [Online].

   Available: https://arxiv.org/abs/2306.11980

7. K. Kaewtawee et al., Cloning a Conversational Voice AI Agent from Call Recording Datasets, 2025. [Online]. Available: https://arxiv.org/abs/2509.04871

8. S. Obadinma et al., Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework, 2023. [Online]. Available: https://arxiv.org/abs/2302.03222

9. T. R. M., V. Kumar, and S.-J. Lim, Intelligent Personal AI Desktop Assistant, 2024. [Online]. Available: https://www.irjet.net/archives/V12/i5/IRJET-

   V12I595.pdf

10. Virtual Assistant, Wikipedia, 2025. [Online]. Available: https://en.wikipedia.org/wiki/Virtual_assistant

11. The Sound of Progress: AI Voice Agents in Service, Journal of Service Management, 2025. [Online]. Available:

    https://www.emerald.com/insight/content/doi/10.1108/J OSM-06-2025-0269/full/html

12. Twilio Inc., Programmable Voice API Documentation, 2024. [Online]. Available: https://www.twilio.com/docs/voice