AutoQC System for Metal Manufacturing Company
Developed an Automated Quality Control (AutoQC) system capable of detecting cracks in metal components. Leveraging edge computing, this solution ensures a cost-effective and modular deployment, allowing real-time monitoring and defect identification at production sites.
This system enhances manufacturing precision and reduces downtime by eliminating the need for expensive cloud infrastructure, providing scalable quality assurance across multiple production lines.
QHSE Detection System for Construction Company
QHSE Detection System for Construction Company
QHSE Detection System for Construction Company
Engineered a real-time Quality, Health, Safety, and Environment (QHSE) detection system using streaming cameras. The AI system monitors the workforce to ensure compliance with safety standards, including wearing helmets, vests, boots, and uniforms.
This system enhances manufacturing precision and reduces downtime by eliminating the need for expensive cloud infrastructure, providing scalable quality assurance across multiple production lines.
Face Recognition System for Software Company
Designed and implemented a highly secure and efficient face recognition system for access control and identity verification in a software development environment. The system provides seamless user authentication while ensuring privacy and data security through advanced encryption methods.
By leveraging deep learning models, the system achieves high accuracy in identifying individuals even in low-light or partially occluded environments, making it an essential tool for improving workplace security and enhancing employee access protocols.
Violence Detection in Surveillance Systems
Violence Detection in Surveillance Systems
Violence Detection in Surveillance Systems
Developed an AI-driven surveillance solution that automatically detects acts of violence in real-time through video streams. The system employs deep learning algorithms to analyze behaviors and identify potential violence, alerting security personnel instantly.
This technology improves public safety by enabling proactive responses in critical environments such as public spaces, schools, and corporate facilities.
Document Analysis for Information Extraction
Created a document analysis system that automates the extraction of key information from structured and unstructured documents. Utilizing natural language processing (NLP) and machine learning models, the system is capable of analyzing various document formats, extracting critical data points, and organizing them into usable formats for further analysis.
This solution streamlines document processing workflows, reduces manual errors, and accelerates decision-making processes in industries like legal, finance, and healthcare.
RAG Chatbot (Retrieval-Augmented Generation)
RAG Chatbot (Retrieval-Augmented Generation)
RAG Chatbot (Retrieval-Augmented Generation)
Developed a Retrieval-Augmented Generation (RAG) chatbot capable of answering complex queries by retrieving relevant information from vast knowledge bases. The system integrates advanced language models with retrieval mechanisms to provide accurate and context-aware responses.
This chatbot enhances customer support services and knowledge management by delivering high-quality interactions that reflect realtime information from various sources.
Data Audit Classification for a Hospital
Implemented an AI-based data auditing system for a hospital to ensure compliance with healthcare standards and regulations. The classification model audits patient records and medical documents, flagging any inconsistencies or potential violations.
This system improves data integrity, enhances the efficiency of medical data audits, and helps the hospital maintain a high standard of healthcare compliance.
Designed an AI system to assist in evaluating trademarks (Merk) for the DJKI (Directorate General of Intellectual Property). The system automates the analysis of trademarks, ensuring that new applications comply with legal standards and identifying potential conflicts with existing trademarks.
This solution significantly reduces the workload for human evaluators and improves the speed and accuracy of trademark evaluations.
