![images](https://stringspace.s3.ap-south-1.amazonaws.com/ai-chat-bot.png)
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Project Title
Client-Centric Generative AI Chatbot
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Timeline:
~ 30 Days
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Tech Stack:
Python
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Demo Link:
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Client Name:
Project Detail:
Developed a state-of-the-art generative AI chatbot focused on understanding and responding to client-specific queries. The chatbot has been seamlessly integrated across multiple platforms including a website, mobile app, and WhatsApp to enhance user engagement and provide real-time assistance.
Problem Statement:
In the modern digital age, clients expect immediate responses and tailored interactions when seeking information. There was a growing need for an intelligent system that could comprehend client-specific data, provide instantaneous responses, and be available across various digital platforms.
Solution:
We leveraged Generative AI techniques to build a chatbot capable of generating responses based on the context provided by the client. This ensures dynamic and tailored interactions. The chatbot was further integrated across the website, mobile app, and WhatsApp to ensure wide accessibility and consistent user experience.
Features:
- Dynamic Responses: Generates tailored responses based on client context.
- Multi-platform Integration: Available on website, mobile app, and WhatsApp.
- Real-time Engagement: Instantaneous replies to client queries.
- Context Preservation: Remembers past interactions to ensure continuity in conversations.
Use Cases:
- Customer Support: Address client queries round-the-clock without human intervention.
- Product Information: Provide detailed product info or recommendations based on client preferences.
- Feedback Collection: Seamlessly collect feedback and reviews from clients.
- Lead Generation: Engage potential clients visiting the website or app.
Data Science Specific Points:
- Data Collection: Utilized client interactions, historical chat logs, and feedback to train the model. Ensured that all data collected respected privacy regulations.
- Data Analysis: Employed Natural Language Processing techniques to understand the context, sentiment, and intent of user messages. This ensured the bot could generate appropriate responses.
- Results: The chatbot achieved a high user satisfaction rate, reduced the need for human support agents during off-hours, and increased engagement on all integrated platforms.
Technologies Used:
- AI/ML Frameworks: TensorFlow, GPT-3.5
- Chatbot Development: Rasa, IBM Watson X
- Integration Tools: WhatsApp Business API, Mobile App SDKs, WebSockets
- Cloud: Azure Functions (for serverless computations)