HONG KONG, July 2, 2026 /PRNewswire/ -- CLPS Incorporation (the "Company" or "CLPS") (Nasdaq: CLPS) today announced the launch of Athena, its enterprise AI knowledge assetization project designed to enhance operational efficiency, improve service quality, and safeguard the Company's core intellectual assets.
Leveraging advanced technologies including multimodal knowledge extraction, large language models (LLMs), vector databases, self-evolving artificial intelligence (AI) agents, and LLM Wiki knowledge weaving, Athena consolidates critical knowledge assets—such as product capabilities, solution implementations, testing methodologies, and AI best practices—that were previously dispersed across documents, images, audio recordings, and Customer Relationship Management (CRM) systems into an interactive, reasoning-enabled intelligent knowledge base.
Athena is designed to empower the Company's sales, pre-sales, delivery, and client management teams by improving client service efficiency, solution development quality, and strategic decision-making capabilities, while establishing a long-term competitive advantage through the protection and utilization of the Company's core intellectual assets.
Multimodal Perception: From People Searching for Documents to Systems Understanding Knowledge
Over the years, the Company has accumulated extensive knowledge assets, including product materials for solutions such as CAKU and Nibot, financial and non-financial industry solutions and testing expertise, e-commerce and banking delivery experience, AI methodologies, and other institutional knowledge. These resources are stored across multiple formats—including Word documents, PDFs, PowerPoint presentations, spreadsheets, images, and audio files—and distributed across various locations.
Through technologies such as optical character recognition (OCR), speech recognition, document layout analysis, and semantic summarization, the platform automatically extracts and structures heterogeneous information. The information is then vectorized, segmented, embedded, and stored within a vector database, creating a unified knowledge foundation.
Knowledge Weaving: From Document Repositories to Knowledge Networks
Fragmented knowledge bases remain a significant operational bottleneck for modern enterprises, often forcing users to manually synthesize information across isolated documents. Athena directly addresses this inefficiency by leveraging advanced LLM Wiki pattern, which automatically transforms unstructured, raw documents into a seamlessly navigable, interlinked knowledge network. Consequently, when users query the system, Athena bypasses traditional disjointed search results. Instead, it retrieves and contextualizes answers directly from within this structured network, delivering comprehensive and actionable insights.