Project details

TutorCat is a high-performance Intelligent Systems platform designed to orchestrate large language models (LLMs) for personalized, real-time educational inference. The architecture focuses on bridging the gap between raw generative AI and pedagogical accuracy by implementing sophisticated NLP pipelines and predictive analytics. This project serves as a technical benchmark for delivering scalable, data-driven personalization that directly impacts user retention and learning outcomes. Learn more about TutorCat on the App Store.

Key Systems Architecture

LLM Orchestration Engine: Designed and deployed a modular inference layer leveraging GPT-based architectures to synthesize step-by-step academic solutions across diverse subject domains.
Predictive Personalization Layer: Engineered an analytics engine to monitor student performance metrics and dynamically adapt learning trajectories through Scikit-learn and custom predictive models.
High-Impact Recommendation Framework: Architected a content recommendation system that achieved a 35% increase in user engagement by delivering tailored study materials based on real-time performance data.
Advanced NLP Pipelines: Utilized Hugging Face and PyTorch to fine-tune deep learning models for accurate intent recognition and pedagogical response synthesis.
Multi-Model Fine-Tuning: Orchestrated the training and optimization of models using TensorFlow, focusing on reducing inference latency while maintaining high accuracy across complex curricula.

Technical Leadership & Ownership

Systemic AI Integration: Architected the bridge between raw student queries and fine-tuned LLM responses, ensuring production-grade reliability.
Data-Driven ROI: Transformed behavioral data into a scalable recommendation engine that significantly moved core business metrics.
Model Lifecycle Management: Managed the full lifecycle of deep learning models, from initial R&D and training in PyTorch to production-scale deployment.

TutorCat: AI Homework Help

Project details