Yousef Ferwana

Problem

Students needed instant guidance and answers without waiting for administrative support.

Solution

Designed and deployed a Chatbot + Voice Agent powered by a custom-trained AI model integrated into a university-level learning platform.

Result

Fully automated student support system with improved engagement and real-time interaction.

Problem

Over 10,000 JSONL files were used to train 6+ models. All models generated unstable, low-quality results due to inconsistent and poorly structured data.

Solution

• Identified data quality as the root issue
• Built a custom Python pipeline to clean and standardize the dataset
• Augmented structured data using AI-assisted generation
• Fine-tuned Llama 3.2 8B using QLoRA
• Integrated the improved model into a live web application

Result

Stable, production-ready AI model delivering high-quality outputs with significantly improved performance.

Problem

New incoming data files were messy, inconsistent, and required manual intervention before retraining.

Solution

Developed an automated Python-based preprocessing pipeline that cleans, validates, structures, and standardizes all incoming JSONL files automatically.

Result

Retraining-ready datasets generated instantly with zero manual overhead.

Problem

A business required domain-specific insights from a large language model while maintaining efficiency and precision.

Solution

• Developed a custom AI agent powered by Qwen3-8B
• Applied LoRA fine-tuning for domain adaptation
• Optimized inference efficiency
• Structured retrieval pipeline for accurate responses

Result

A highly efficient, domain-adapted AI agent delivering precise insights and deployed in a production environment.