CLPS Incorporation (the "Company" or "CLPS") (Nasdaq: CLPS) 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.

Intelligent Interaction: From Keyword Search to Direct Answers

Traditional knowledge retrieval relies heavily on keyword matching and often struggles to address the diverse needs of different roles, including sales, pre-sales, and delivery teams. Through Athena, employees can simply ask questions in natural language. Intelligent agents automatically perform retrieval, reasoning, and planning processes to deliver precise answers rather than lists of documents, significantly reducing the time and effort required to access information.

Athena supports multi-turn dialogue and contextual understanding, providing targeted responses based on conversational context—much like a knowledgeable colleague with access to the Company's collective expertise. For high-frequency scenarios such as client visits, pre-sales presentation preparation, and bidding strategy development, Athena enables near real-time knowledge support.

Self‑Evolving Feedback Loop and Permission Isolation: Ensuring Answer Quality and Data Security

The platform incorporates a built-in feedback mechanism that allows users to provide positive or negative evaluations of responses. Automated inspection agents periodically analyze feedback data, diagnose potential issues, and optimize the knowledge base structure, retrieval strategies, and agent workflows. Through self-evolving technology, the platform continuously improves over time, becoming more accurate with ongoing usage.

At the same time, Athena has implemented a strict permission isolation framework based on business hierarchy and confidentiality levels, ensuring that employees can access only the knowledge within their authorized scope while maintaining an appropriate balance between information sharing and data security.

Triple Value: Operational Efficiency, Asset Protection, and Innovation-Driven Growth

• Operational Efficiency Gains: Employees no longer need to conduct repetitive cross-functional inquiries or manually search through massive documentations. Knowledge retrieval time can be reduced from hours to seconds, and the Company expects pre-sales solution delivery efficiency to improve by more than 50%.

• Core Asset Protection: Athena transforms accumulated institutional knowledge into a company-wide digital asset, effectively mitigating the risk of knowledge loss due to employee turnover while strengthening the Company's long-term competitive advantage.

• Decision-Making and Innovation Enablement: By aggregating and correlating knowledge across projects, business functions, and industries, the platform can uncover hidden business patterns and growth opportunities, providing data-driven insights to support product innovation, market expansion, and strategic decision-making.

Ms. Zhao Jing, Project Lead, commented: "The rapid advancement of AI technologies over the past year has enabled AI to become a core driver of enterprise innovation and decision-making. Athena is focused on embedding these capabilities into the Company's daily operations. In the past, enterprise knowledge bases were like parking lots for documents—large volumes of information were stored, but it was difficult to find and even harder to utilize. Through Athena, we are transforming the knowledge base into a factory for knowledge. Rather than simply storing documents, AI automatically understands, weaves together, and connects information, turning every document into a node within a knowledge network. Every question employees ask helps make the entire knowledge network smarter."

Mr. Raymond Lin, Chief Executive Officer of CLPS, stated: "Through the AI knowledge assetization project, we are transforming our fragmented core data into a reasoning-enabled, self-evolving intelligent hub that enables employees to obtain precise answers in seconds and significantly improves operational efficiency. At the same time, we are safeguarding the Company's intellectual assets and reducing the risk of knowledge loss associated with workforce mobility. We believe this initiative will substantially enhance pre-sales solution development efficiency and client responsiveness.

"Looking ahead, we envision equipping every employee with an AI-powered digital assistant capable of providing comprehensive access to the Company's collective knowledge. We also plan to explore commercializing these capabilities externally, helping more enterprises unlock the value of their knowledge assets and accelerate their digital transformation journeys."