AI Chatbot for Customer Support Automation
This AI chatbot helps businesses automate customer queries and reduce response time by up to 60%.
🔹 Problem:
Businesses often struggle with delayed responses, repetitive queries, and high support workload.
🔹 Solution:
Developed an AI chatbot using NLP that can understand user intent and provide instant, accurate replies.
🔹 Key Features:
• Handles common customer queries automatically
• Basic intent recognition using NLP
• Fast and real-time responses
• Easy integration with web/mobile apps
🔹 Result:
• Reduced response time
• Improved customer experience
• Lower support workload
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đź“Ś Project Title
Used Car Price Valuation AI — Machine Learning Resale Estimator
đź’ˇ Overview
The secondhand automotive market faces significant price opacity, making accurate valuation challenging for individual sellers, buyers, and dealership networks.
To solve this problem, I built an end-to-end Machine Learning web application that automates vehicle market price discovery. The application ingests 11 core vehicle specifications — including brand model variant, city location, registration year, driven distance, fuel type, transmission type, owner history, fuel efficiency, engine displacement (CC), and horsepower (BHP) — to deliver instant, reliable market price predictions in Lakhs INR alongside an estimated fair-market value range.
⚡ Key Features & Deliverables
High-Accuracy ML Regression Engine: Powered by Scikit-Learn HistGradientBoostingRegressor trained on thousands of vehicle transactions with custom label encodings for 200+ car models across 11 metropolitan markets.
Real-Time Interactive Valuation Dashboard: Built with Streamlit featuring live input sliders, numerical controls, and instant price calculations.
Interactive Resale Value Range & Gauge Visualization: Integrated dynamic Plotly gauge charts providing a visual market valuation indicator and a condition-based fair market range (+/- 8% variance band).
Figma-Grade Human Design Theme: Designed a custom CSS UI system (Obsidian Slate & Indigo Emerald theme: #0B0F19 background, #6366F1 indigo accents) with Plus Jakarta Sans typography and translucent glassmorphic cards.
🛠️ Tech Stack
Python 3.14 • Scikit-Learn • Streamlit • Plotly • Pandas • NumPy • Joblib • CSS3 Glassmorphism
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Software Engineering Job Simulation (JPMorgan Chase & Co.)\
I completed a rigorous software engineering simulation where I worked on real-world engineering tasks. This experience focused on financial data visualization, system architecture, and implementing technical solutions to meet industry-standard requirements. It refined my ability to write clean, maintainable code for complex enterprise environments.
Key Contributions:
Data Visualization: Developed tools to visualize live financial data for better decision-making.
System Design: Analyzed and implemented fixes for existing codebases to improve performance.
Industry Standards: Applied professional software development life cycle (SDLC) practices.
Skills: Python, Technical Analysis, Data Visualization, Git.
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Designed and developed AI-powered solutions including intelligent dashboards, workflow automation systems, AI agents, chatbots, lead qualification systems, and GPT-powered business tools.
Capabilities:
• AI Agents
• OpenAI Integration
• Workflow Automation
• AI Dashboards
• GPT Applications
• Lead Automation Systems
• Internal Business Tools
Technology:
OpenAI, LangChain, Python, Vector Databases, APIs, Automation Platforms
Industries:
Startups, SaaS, Healthcare, Enterprise
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AI Support Email Automation & Model Testing
Tested AI models for handling high-volume customer support emails and automatically classifying customer issues.
The work compares models based on:
• Classification results
• Response stability
• Processing latency
• Estimated monthly cost
The testing also included a real-world support email containing multiple issues, such as a payment problem, duplicate charge, missing order confirmation, and app crash.
The goal is to identify an AI approach that can support reliable email classification and automated routing at scale.
Skills:
AI Automation
Artificial Intelligence
Data Analysis
Tools:
Only select the tools you actually used for this work, such as:
Python
Google Colab
OpenAI
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Built an AI Interview System that works end-to-end
I recently developed a complete AI-powered interview platform that automates the entire hiring workflow.
• Real-time question generation
• AI-based answer evaluation
• Instant report generation after interview
• Smooth and structured user experience
This system helps recruiters reduce manual effort and make faster, data-driven decisions.
Tech Stack: Python, Django, React.js, LLM Integration, Automation
Currently working on improving multi-user testing and enhancing evaluation accuracy through better prompt engineering.
Open to freelance opportunities in AI systems, automation, and full-stack development.
Feel free to connect if you’re building something similar.
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