AI Automation & Chatbot Developer | Flutter Developer
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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510
Created a short AI-based video showcasing how artificial intelligence is transforming the way we work and create.
Worked on concept development, video editing, and storytelling. Also highlighted my experience in AI training and sentiment analysis.
#AI #ArtificialIntelligence #VideoEditing #DataAnalysis #ContentCreation
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258
I worked on a Sentiment Analysis project where I classified customer reviews into positive, negative, and neutral categories. I performed text preprocessing, used TF-IDF for feature extraction, and applied a machine learning model to improve accuracy. This project helped me gain hands-on experience in NLP, data labeling, and model evaluation.
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3
382
Built an AI-powered resume analyzer that evaluates resumes and provides instant feedback to improve job success rates.
The system analyzes resume content, structure, and keywords using AI to generate a score and actionable suggestions. It helps users optimize their resumes based on industry standards and ATS (Applicant Tracking System) requirements.
This tool is designed for job seekers and professionals to enhance their resumes and increase their chances of getting shortlisted.
"Open to building similar AI-powered tools for businesses". 🚀
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273
ML & AI Engineer | Computer Vision | LLM Fine-Tuning & RAG
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
0
34
This research-based computer vision system automates the digital transcription of analogue gauge readings from video input. By pairing deep learning keypoint detection with geometric perspective correction, the system accurately reads dial values in real time and streams logged metrics directly to cloud-based monitoring tools.
Key Technical Contributions & Features
1. YOLOv8 Keypoint Detection Pipeline: Trained a YOLOv8-pose/keypoint model to precisely identify four critical reference coordinates on the gauge dial—the scale minimum point, scale maximum point, pointer center, and pointer tip. Achieved 93% mAP across a hybrid dataset of 10,000 synthetic gauge images and 2,700 annotated real-world video frames.
2. Parallax & Geometric Distortion Correction: Designed a custom geometric mapping algorithm to overcome 3D parallax errors—where non-coplanar pointers and scale faces skew readings from off-center camera angles. The algorithm computes angular sweeps and normalizes scale orientation for precise angular-to-value conversion.
3. Real-Time Video Analytics Backend: Built an automated video processing pipeline using OpenCV and NumPy to capture live feeds, track pointer displacement continuously, and transcribe visual metrics into structured digital numbers.
4. Automated Cloud Logging: Integrated real-time data exporters to continuously push transcribed gauge values to cloud spreadsheets (Google Sheets / Excel) for continuous tracking and telemetry monitoring.
Tech Stack
Languages & Core: Python, NumPy
Computer Vision & Image Processing: OpenCV
Deep Learning & Machine Learning: YOLOv8 (Keypoint Detection), Convolutional Neural Networks (CNN), Scikit-Learn
Data Integration: Google Sheets API
0
70
SOYFIB is a computer vision platform engineered to automate the detection and classification of soybean leaf diseases. Using a novel, hybrid deep learning architecture, the platform extracts high-dimensional visual signatures from crop imagery to perform zero-shot disease identification and provides interactive tools for researchers to dynamically expand the diagnosis database.
Key Highlights & Engineering Contributions
Hybrid Feature Extraction Pipeline: Built a deep learning vision pipeline combining a custom CNN with a Swin Transformer architecture to extract 512-dimensional visual disease fingerprints from soybean leaf images.
Dynamic Zero-Shot Detection Backend: Implemented a zero-shot diagnosis system by indexing extracted feature embeddings into a FAISS vector database, enabling ultra-fast vector matching and similarity retrieval.
Custom Dataset Preprocessing: Built an end-to-end data pipeline to handle inputs of 225 X 225 X 3 dimensions and parse YOLO-formatted annotations (.txt), optimizing feature extraction accuracy.
Web Platform & Researcher Portal: Developed a Flask web application allowing users to upload geolocated crop images and view real-time diagnostics, paired with an admin portal for researchers to register new disease vectors dynamically.
0
81
EyeSense is an enterprise-grade, real-time AI surveillance system designed for automated visitor monitoring and security tracking. The system replaces manual check-in processes by utilizing computer vision techniques to detect, track, and recognize individuals across multiple concurrent video streams, reducing manual security workloads by approximately 60%.
Key Features & Technical Architecture
Multi-Stream Processing: Engineered a low-latency video pipeline using OpenCV operating at 25 FPS, capable of processing 4 active CCTV feeds concurrently without frame drops or memory leaks.
Face Recognition & Indexing: Implemented identity matching by extracting high-dimensional embeddings using HuggingFace FaceNet models and indexing them into a FAISS vector database. Achieved sub-100ms vector matching across a database of 500+ identity profiles.
Automated Alerting Engine: Designed a real-time event engine that flags unrecognized or unauthorized individuals within 3 seconds, automatically triggering instant security alerts via SMS and email.
Management Dashboard & Containerization: Developed a Flask-based web platform for system administration, access logging, real-time visitor approval, and monitoring. Containerized the entire application stack using Docker for standardized deployment across hardware environments.
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58
Full-Stack Architect & SaaS Founder | Building AI Systems
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
Academic Web Portal Modernization (DAVV)
I completed an institutional internship focusing on overhauling and upgrading the official web platform for the School of Life Sciences at DAVV. The goal was to transform a legacy system into a responsive, fast, and user-friendly portal for academic content delivery. My work ensured better layout hierarchy, clean frontend structure, and seamless navigation for high-volume user traffic.
1
61
GetYourQR: AI-Powered QR Menu & Smart Billing SaaS
I built and launched this full-stack platform to modernize the hospitality industry. My focus is on high-efficiency development and using modern frameworks to deliver scalable, production-ready systems with speed and precision.
Key Features:
Dynamic QR menu generation with real-time updates.
Automated billing and order management system.
High-performance cross-platform mobile experience.
2
2
190
OnlyTurf: Integrated Sports Booking & Venue Management System
I developed a dual-platform ecosystem designed to streamline sports facility bookings. This project includes a high-performance mobile app for users to discover and book slots, and a comprehensive web dashboard for vendors to manage their venues, schedules, and payments.
Key Features:
Dual-Interface: Specialized flows for both sports enthusiasts and facility owners.
Real-time Booking: Instant slot reservation with live availability updates.
Vendor Management: Robust administrative tools for slot scheduling and revenue tracking.
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83
AI/LLM Developer | Python & FastAPI | Custom AI Web App
AI Automation & Workflow Solutions
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
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.
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60
AI Business Automation
A practical AI and workflow automation solution designed to reduce repetitive work and connect business processes.
This work covers lead automation, CRM integration, AI assistants, data automation, reporting, customer support workflows, and tool integrations.
The approach is simple: understand the existing process, identify what can be automated, build the workflow, connect the required tools, and test the solution.
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61
Lead & CRM Workflow Automation
A business workflow concept designed to automate the process from lead capture to CRM updates and follow-up.
The workflow connects incoming leads with automated data processing, lead qualification, CRM updates, personalized follow-ups, and internal notifications.
The goal is to reduce repetitive manual work, keep customer information organized, and ensure leads are followed up consistently.
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31
Data Analyst| SQL, Python & Data Visualization
Full-Stack Developer building scalable solutions
Full-Stack Developer building scalable solutions
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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83
Maedric is a Singapore-based jewellery brand that specializes in coloured gemstones like ruby, sapphire, and emerald, along with custom-made jewellery pieces.
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76
"Experienced freelancer with over 8+ years
"Experienced freelancer with over 8+ years
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