Overview
KaiKnowledge Management: AI-enabled predictive and perspective analysis tool
KaiKnowledge Management is an AI-enabled tool that adds knowledge to copilots, assistants, and analysis. It is designed to manage knowledge, generate insights, and predict outcomes from both structured and unstructured data. Users can feed the tool with a wide range of information in various formats, such as PDFs, Word documents, PowerPoint presentations, and videos. This diverse data is compiled into a comprehensive knowledge base. Utilizing a Multi-Agent Hybrid-search accelerator that incorporates Generative AI, the tool efficiently handles complex queries through its chat feature, which leverages Generative AI to provide accurate answers by searching the knowledge base.
The system employs vectors, knowledge graphs, and generative AI to create Relationship Graphs that map connections between various data points, enhancing the overall quality of the knowledge base. By deploying a hybrid search approach, it combines chunking or vectors with knowledge graphs for optimal results.
KaiKnowledge Management builds multiple agents that collaborate to recommend the model with the highest accuracy for various predictive outcomes. All generated Relationship Graphs from both structured and unstructured data are stored in a Master Database, which is continually updated to facilitate hybrid search and predictive analytics. Overall, KaiKnowledge Management simplifies the handling of data, offering a seamless experience for generating insights, answering queries, and visualizing data connections. Its advanced AI capabilities not only help keep the knowledge base current but also suggest the most accurate models for user needs, transforming the way organizations manage and understand their information for meaningful conclusions, decisions, and predictions.
One of the use cases of this KaiKM is dealing with structured data. Generative AI can work on unstructured data but it can also be used for predictive and perspective analysis both with a partial input as textual query from Users. Users can upload CSV files to train the model and receive output almost instantly. Users can choose from various models, such as classification, clustering, or regression, based on their needs, with the system providing performance metrics like accuracy, precision, recall, and F1 scoring.
