ThinkSphere: A Privacy-Preserving Semantic Network Architecture for AI Democratization in SMEs
DOI:
https://doi.org/10.33423/jlae.v22i3.7916Keywords:
leadership, accountability, ethics, semantic indexing, decentralized architecture, privacy-preserving AI, multi-client system, plug-and-play widget, vector embedding, AI-native querying, data sovereignty, organizational learning, ethical AI, leadership transformation, business democratization, plug-and-play AI, SME digital adoptionAbstract
ThinkSphere offers a new, simple way for AI to find information. It’s designed to assist small and medium-sized businesses (SMEs) with significant challenges in leadership, data governance, and the responsible use of AI. Currently, AI often means losing control of your data, incurring excessive costs for data preparation, and lacking flexibility.
Our system has three main parts: Core, Client, and Widget. This design separates who owns the data from how AI understands it. This new system helps SMEs use AI more easily. It lets them make smarter decisions and better manage their business. It also makes sure data is used responsibly and helps leaders make quick choices based on real-time information. Early tests show that ThinkSphere can offer an AI solution that is easy to get, saves money, and is ethical. This helps make AI available to more businesses, especially SMEs.
