Flagship AI Projects
AI-driven solutions have been effectively implemented at Intel, Samsung, and Accenture, targeting areas such as automation, content moderation, and intelligent document processing. These projects have achieved tangible results, including a 60% reduction in cycle times and significant operational savings. Each platform addresses unique business needs through advanced technologies like computer vision, generative AI, and machine learning. The range of solutions includes tools for internal enterprise use as well as customer-facing systems, all tested in real-world production environments. A functional prototype, developed using agile proof-of-concept coding, is available for public demonstration
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Product Requirement Generator
Problem: Manual requirement generation from dense technical documents is slow and prone to errors, leading to costly validation escapes.
Solution: An intelligent RAG-based tool that parses specifications, understands context, and auto-generates precise, verifiable validation requirements.
Impact: Reduced validation cycle time by 60% and saved an estimated $1 million annually at Intel.
View on GitHub →
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Debug Accelerator Suite
Problem: Engineering teams spend excessive time on manual bug triage, slowing down issue resolution.
Solution: A unified GenAI platform that automates duplicate bug detection, classifies issues, and provides root-cause analysis suggestions across JIRA and HSDES.
Impact: Reduced manual triage time by 60% and boosted triage precision by 10%.
Internal Enterprise Tool
🖼️
Content Moderation Engine
Problem: Global brands require scalable solutions to protect users from inappropriate content.
Solution: A deep learning tool using Vision-Language models (CLIP) to moderate user-generated images at scale, ensuring brand safety.
Impact: Delivered $1 million in operational savings for a global retail client.
View on GitHub →
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AI Wafer Inspection System
Problem: Manual inspection of semiconductor wafers for microscopic defects is slow and cannot scale with modern fabrication demands.
Solution: A computer vision platform that uses high-resolution imaging and CNNs to automatically detect, classify, and map defects on wafers in real-time.
Impact: Increased defect detection accuracy by 25% and boosted inspection throughput by over 200%.
View on GitHub →
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Driver Drowsiness Detection System
Problem: Driver fatigue is a leading cause of traffic accidents.
Solution: An edge AI system using a lightweight camera and facial landmark analysis to monitor signs of drowsiness (eye closure, head pose) and trigger real-time alerts.
Impact: Prototyped system demonstrated a 95% accuracy rate in detecting micro-sleep events in simulation.
View on GitHub →
