
Through a domain-optimized retrieval augmented generation architecture for domain-specific knowledge base integration, our technology incorporates a scalable embedding solution for efficient retrieval, enabling resource-adaptive deployment for various enterprise solutions. The system features efficient semantic search utilizing scalable embeddings. Its flexible architecture supports adjustable model depth and embedding dimensions, making it ideal for enterprise-ready deployment across various specialized domains.
In today's rapidly evolving AI landscape, organizations face significant challenges when deploying large language models (LLMs) in specialized domains. These challenges include hallucination issues, high computational costs, and the difficulty of integrating domain expertise while managing resource constraints in enterprise deployment.
Lingnan University, a venerable institution in Hong Kong's academic landscape, has a rich heritage that dates back to its founding in Guangzhou in 1888. Known in its earlier years as Lingnan Xuexiao and subsequently as Lingnan University, the institution flourished in the field of higher education until 1952. It was reborn in Hong Kong in 1967 and has since aspired to evolve into a distinguished research-focused liberal arts university for the digital age. Lingnan is committed to excellence in teaching, learning, research, and fostering community ties, aiming for international acclaim.