What I have recently felt through exhibitions and various business negotiations is that the utilization of generative AI is becoming not just a means of improving operational efficiency, but rather a fundamental strategy for achieving significant performance enhancement for companies. However, many companies are caught in a serious dilemma where, while there are directives from management to "actively adopt AI," the information systems department and on-site staff are hesitant due to various concerns. The background to this includes fears of confidential information leaks, the complexity of various business flows that have become difficult for on-site staff to explain, and subscription models that are hard for management to predict, such as pay-per-use based on token quantities, which are structural risks associated with traditional cloud-based AI. Therefore, in this blog, I will outline the "four critical challenges" that companies face when they begin to fully utilize AI, and introduce the advantages of a "completely closed platform" as a strategic option to address these challenges.

The term "generative AI" has become something we hear in our daily lives. Generative AI, including ChatGPT and Gemini, is being utilized in various situations such as writing and summarizing text, information retrieval, image generation, and program creation. In companies, expectations for generative AI are rising for purposes like improving operational efficiency, utilizing information, and creating new services. On the other hand, because generative AI has become more accessible, many people may want to clarify the basics: "What exactly is generative AI?" "What is different from previous AI?" "How can it be specifically utilized in companies?" "What kind of environment is necessary to fully leverage generative AI?" This time, as the use of generative AI in businesses is progressing, we will revisit and organize our understanding of generative AI, its potential, and the computing environment that supports it.

In recent years, the use of "generative AI" has rapidly advanced as a trump card for business efficiency. However, when companies try to implement it seriously within their organizations, they often encounter various "walls." "What if customer data or confidential information leaks to the outside...?" "The costs of pay-per-use increase the more we use it, which is burdensome..." "What if the information generated by AI is false (hallucination)...?" As a breakthrough that simultaneously breaks through these three walls of "security," "cost," and "reliability," the combination of "Local LLM × RAG (Retrieval-Augmented Generation)" is currently receiving a lot of attention. In this blog, we will thoroughly explain the topic of 'Local LLM × RAG'!

Recently, there has been a significant increase in inquiries from companies that are advancing DX in manufacturing sites, stating, "We are considering a local LLM to safely handle confidential information such as CAD/CAM-related data, design drawings, specifications, processing condition tables, and technical documents!" Indeed, a local LLM that does not send data outside is a very strong option in terms of security. However, when actually starting operations, it is necessary to anticipate the possibility of encountering new obstacles such as: "If there is a misinterpretation of drawings due to hallucinations (plausible falsehoods), it will ultimately be a waste of time and unusable." "We want to establish a system that allows for the safe utilization of AI by setting conditions such as strict internal drafting rules and compliance with JIS standards." The local AI platform equipped with hallucination reduction features and safety functions aims to solve these challenges in a one-stop manner and maximize the value of local LLMs. In this article, we will explain why GENFLUX Local can be considered an "essential partner" for the operation of CAD/CAM-related data and design/manufacturing data with local LLMs.