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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34
Comprehensive validation testing, proof-of-concept verification, and structural remediation audits to ensure complete security patch efficacy.
The Objective:
A development organization required a rigorous follow-up assessment to validate that security patches introduced after an initial audit were correctly implemented, completely neutralized the identified threat vectors, and did not introduce new regression flaws into the environment.
The Approach:
I executed a dedicated verification cycle focused specifically on replicating the original attack patterns using the exact tools and exploits from the initial assessment. Every patch was manually tested using Burp Suite Professional, custom Python exploit scripts, and runtime debuggers to confirm the vulnerability's status and measure remediation success against standard CVSS 3.1 metrics.
From a dedicated retesting scope targeting 20 original security findings, I delivered the following lifecycle results:
Closed (Remediated):Â Successfully verified the resolution of 16 findings, including all critical business logic bypasses and sensitive data exposure pathways, confirming they are no longer exploitable.
Bypassed (Incomplete Fixes):Â Identified 2 instances where a development team used client-side input validation instead of server-side sanitization, allowing the original exploit to still bypass the patch.
New Regression:Â Discovered 2 minor configuration errors introduced by changes made to the access control framework during the patching process.
Documentation Provided:Â Delivered updated Proof-of-Concept (PoC) logs and step-by-step documentation for the outstanding issues to guide the team to a permanent fix.
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105
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.
1
109
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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45
A real estate business needed an efficient system to collect, enrich, and manage high-quality sales leads from public sources — reducing manual work and enabling seamless CRM integration. We built a targeted lead collection and enrichment system, extracting publicly available data, enhancing each lead with company name, role, and industry details, and preparing everything for direct import into HubSpot and Salesforce.
âś… Industry-specific lead collection using keywords and demographics
âś… Lead enrichment with company name, role, and industry data
âś… HubSpot & Salesforce ready CSV export
âś… Scalable, repeatable lead pipeline
Result: Client significantly improved productivity, cut manual lead tasks, and scaled outreach with organized, enriched data ready for immediate use.
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203
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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30
