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The "Conata Data Agent" is a data utilization assistant that quickly and easily derives insights by traversing various data within the organization. Utilizing advanced natural language processing and generative AI, our service accurately understands intentions even from complex questions and provides the questioner with new insights and hidden discoveries. By conversing with the agent, users can also summarize the necessary information and visualize numerical data. 【Features】 ■Improves business productivity ■Effectively utilizes multiple data sources simultaneously ■Allows for easy self-analysis *For more details, please download the PDF or feel free to contact us.

• Big Data

This document is a white paper that clarifies the actual state of AI utilization in the BtoB sector based on survey data, providing essential know-how to achieve AI utilization that leads to results. Currently, the shortage of AI talent is seen as a bottleneck, but the real challenge lies in the transition from the implementation phase to the operational phase (establishment and improvement). While rule development is progressing, the assurance of data quality and reliability has not kept pace, revealing a structure where improvement cycles do not operate due to the inability to measure ROI and outcomes. This document explains the importance of an AI-ready data infrastructure and how to visualize your company's current position using frameworks such as FAIR Assessment and FAIR Index. [Contents] ■ The actual state of AI utilization in the BtoB sector ■ The real barriers to promoting AI ■ The importance of an AI-ready data infrastructure *For more details, please download the PDF or feel free to contact us.

• Big Data

The hallucinations have decreased, and the manual verification workload by "people" has reduced. The reference presentation rate has improved, and the accuracy of responses has increased. We will introduce a case of "AI-Readiness" that addresses the concern of "image data not being searchable." In our support for AI projects in the manufacturing industry, a common issue we hear is, "We have attempted to implement generative AI and build RAG, but we are not getting the desired output." By translating complex internal documents into a "system language" that AI can understand through "AI-Readiness," we can build a pipeline for data preprocessing. This enables existing RAG and AI models to decode input data in detail, leading to solutions. [Challenges] ■ Projects are halted due to multiple factors such as data deficiencies, evaluation, and improvement cycles. ■ Image data is not searchable. *For more details, please download the PDF or feel free to contact us.

• Big Data

While many companies are working on generative AI and data utilization, they are facing challenges such as "not knowing where to start" and "having implemented AI but not seeing business results." The results of the "AI-Ready Diagnosis" conducted by Flywheel, involving over 400 companies, have revealed that the root cause is not a lack of technical skills, but rather a "disconnection" in strategy, data infrastructure, and execution processes. In this webinar, we will unveil the "decisive differences between organizations that succeed and fail in AI utilization," derived from extensive diagnostic data. We will deliver insights from real projects, covering everything from the essentials of data design for evolving into an AI-Ready organization to a roadmap for effectively implementing strategy. Additionally, by answering a simple survey during the seminar, participants will also be able to assess their own AI maturity. We will not just stop at validation but will present the "current state" and "next steps" to directly link AI to business outcomes. *For details and registration for the seminar, please refer to the external link. *For more information, please contact us.

• Big Data

This document is an introductory material summarizing the features and usage images of the "Conata Data Agent." It includes key features such as the adaptive RAG that can adjust industry dictionaries and corporate dictionaries, and a multi-stage LLM pipeline that utilizes LLM multiple times for query interpretation, keyword extraction, and answer generation. Additionally, it contains a solution catalog covering areas such as sales improvement, inquiry response, quality management, and internal search, which can be used as a reference for consideration of implementation. 【Contents (partial)】 ■ The world we want to realize ■ The "management foundation" needed in the VUCA era ■ Roadmap to establish the management foundation ■ Value of the framework in realizing the roadmap ■ Reference: Technical capabilities of the flywheel *For more details, please download the PDF or feel free to contact us.

• Knowledge Sharing System