"Rigorous research transforms practical software engineering into foundational knowledge for future generations."
Gaurav Gaikwad’s research focuses on multi-tenant database scaling, Indic language NLP tokenization, and predictive machine learning models in education.
Empirical analysis of sharding and Redis caching across 100+ schools.
Designing high-concurrency relational database schemas and memory cache layers for enterprise multi-tenancy.
Tokenizing Marathi and Hindi regional literature using vector search models for automated archive classification.
Applying machine learning algorithms to forecast student learning gaps and academic performance bottlenecks.
Investigating the ethical implications of artificial intelligence deployment on human privacy and cultural preservation.
An empirical study detailing database partition strategies, query caching algorithms, and load-balancing architectures deployed in Scholar ERP across 100+ schools and 250,000+ daily active users.
Multi-Tenancy, Database Sharding, Redis Caching, Scalability, Educational Software.
Researching vector embedding models and Natural Language Processing (NLP) tokenization for scanning, indexing, and context-aware retrieval of 19th and 20th-century Marathi and Hindi historical manuscripts.
Indic NLP, Vector Embeddings, Marathi Literature, Cultural Archiving, Gemini API.
"Open research drives software progress. We welcome academic partnerships, peer-review exchanges, and joint whitepaper initiatives."
— GAURAV GAIKWAD • Technology Researcher & Product Architect
University researchers, software architects, and AI scholars are invited to connect for joint research projects.