Project Title
Samsung Washing Machine AI Support Chatbot
Problem Statement
Customers often face difficulties when using washing machines, especially when they encounter error codes, vibration, drainage problems, or maintenance issues. Finding the right solution from a user manual or contacting customer support can take time.
What the Chatbot Does
The Samsung Washing Machine AI Support Chatbot provides customers with quick and easy answers to common washing-machine questions. It acts as a digital support assistant that helps users understand problems and find appropriate troubleshooting information.
Key Uses
Explains common washing machine error codes
Helps users troubleshoot washing problems
Provides guidance for starting wash cycles
Helps with vibration and noise-related issues
Provides drainage and drain-filter guidance
Gives cleaning and maintenance information
Helps users understand common machine problems
Provides quick answers without searching through lengthy manuals
Main Benefit
The chatbot makes washing-machine support faster, easier, and more accessible, helping customers find relevant troubleshooting information when they need it and reducing the need for repeated customer-support enquiries.
0
38
Stop re-typing invoices one field at a time.
DocIntel reads invoices and purchase orders into structured data. Every value is checked in code (format, dates, whether it appears on the page), and anything doubtful goes to a review queue with the source page beside it. A person corrects it, and the original value is kept on record.
• On a real model (Claude Haiku 4.5): 387 of 390 fields correct on 30 synthetic invoices, one run; 2 wrong values were not flagged for review (both on image-only scans)
• The video shows a selected example: a date the model misread, caught by a date check
• 658 automated tests; Python, FastAPI, PostgreSQL
Fictional invoices. Want your invoices read for you? Message me.
0
23
Designed and implemented a robust webhook-based trading automation system that connects TradingView signals to real-time trade execution via the Tradovate API. The system is built using Python and handles signal reception, validation, and order execution in a seamless automated pipeline.
The architecture ensures low-latency communication, secure data handling, and reliable execution of trades without manual intervention. It processes incoming webhook alerts, applies validation logic, and triggers precise API-based trade execution.
Key features include:
• Real-time webhook signal processing (TradingView alerts)
• Python-based signal receiver and validation engine
• Secure and reliable API integration (Tradovate)
• Low-latency execution pipeline
• Fully automated trade workflow (zero manual input)
• Scalable design for multi-strategy integration
This solution is ideal for traders and firms looking to automate their strategies using webhook-driven architecture, enabling faster execution, reduced human error, and consistent performance in live trading environments.
1
239
Problem Statement
Students and parents often have questions about courses, admissions, placements, hostel facilities, campus facilities, transportation, and other college services. Finding the right information can take time and may require contacting different departments.
What the Chatbot Does
The College AI Assistant provides a simple way for students and parents to get quick answers to their college-related questions. It works as a digital college information assistant that is available whenever they need help.
Key Uses
• Provides information about courses and programs
• Helps with admission-related enquiries
• Answers questions about placements
• Provides hostel and campus facility information
• Shares transportation and student support information
• Helps users find relevant college information quickly
• Reduces repetitive enquiries handled by college staff
• Makes important information easier for students and parents to access
Main Benefit
The chatbot makes college information quick, accessible, and easier to understand, while helping the college reduce repetitive enquiries and provide better support to students and parents.
0
50
Developed an automated options trading bot using Python that executes trades on Deriv based on real-time strategy signals and multi-condition validation logic. The system is designed to operate continuously with minimal latency, ensuring fast and accurate trade execution in volatile market conditions.
The bot integrates signal generation, validation, and execution into a single automated workflow. It evaluates multiple technical indicators, applies strict entry conditions, and triggers trades only when high-probability setups are detected.
Key features include:
• Fully automated options trading (Deriv API integration)
• Real-time signal processing and execution
• Multi-indicator validation logic for higher accuracy
• Risk management controls and trade filtering
• Continuous operation with minimal manual oversight
• Scalable architecture for strategy customization
This solution enables traders to automate their options trading strategies end-to-end, improving consistency, speed, and discipline while eliminating emotional decision-making.
1
185
Developed a fully automated trading system for MetaTrader 5 (MT5) that executes trades based on validated multi-indicator signals. The system integrates TradingView-generated signals with a Python-based processing engine, which evaluates multiple technical conditions before triggering precise trade execution.
The Expert Advisor (EA) is designed with advanced trade logic, risk management, and real-time execution capabilities. It ensures consistent performance by removing emotional decision-making and strictly following predefined rules.
Key features include:
• Automated trade execution on MT5 (buy/sell orders)
• Integration with external signal sources (TradingView via webhook)
• Multi-indicator validation (RSI, EMA, ADX, Volume, etc.)
• Built-in risk management (lot sizing, SL/TP control)
• Real-time processing with minimal latency
• Fully customizable strategy logic
This system enables traders to automate their strategies end-to-end — from signal generation to execution — ensuring speed, accuracy, and consistency in live market conditions.
1
197
Engineered a high-performance Python-based trading signal engine designed to generate precise and reliable trade signals using multi-indicator confluence logic. The system evaluates over 12 technical indicators including RSI, EMA, ADX, Volume, and trend-based conditions to identify strong market opportunities while effectively filtering out noise and false signals.
The architecture is built to process real-time market data, validate multiple conditions simultaneously, and trigger only high-probability trade setups. This significantly improves signal accuracy and reduces unnecessary trades.
Key capabilities include:
• Multi-indicator confluence engine (12+ conditions)
• Real-time signal processing and validation
• False signal reduction using layered logic
• Scalable architecture for integration with trading APIs
• Seamless compatibility with automated execution systems (webhooks, APIs)
This system can be integrated with platforms like TradingView, Tradovate, or custom trading environments to enable fully automated trading workflows — from signal generation to execution.
Designed for traders and firms looking to achieve consistent, rule-based, and emotion-free trading through intelligent automation.
2
196
Built a complete end-to-end automated trading system that captures alerts from TradingView, processes them through a Python-based webhook engine, validates multi-condition logic, and executes trades directly via the Tradovate API — eliminating manual intervention and improving execution speed and accuracy.
1
189