Achieving highly reliable generative AI output from vast amounts of company data built on knowledge graphs
KAWASAKI, Japan, June 4, 2024 /PRNewswire/ -- Fujitsu Limited today announced that, to promote the use of generative AI in enterprises, it has developed a generative AI framework for enterprises that will flexibly respond to the diverse and changing needs of companies, and allow for the easy compliance with the vast amount of data and laws and regulations that companies possess. Fujitsu will make available a generative AI framework for enterprises globally as part of the Fujitsu Kozuchi lineup starting July 2024.
In recent years, in addition to general-purpose interactive large language models (LLMs), various specialized generative AI models have been developed. In the enterprise, in particular, there have been barriers to the use of these models. These barriers include difficulties with handling the large scale of data required by companies, the inability of generative AI to meet various requirements, such as cost and response speed, and the need to comply with corporate rules and regulations.
Fujitsu has developed a generative AI framework for enterprises to strengthen specialized AI that is able to solve these issues for companies. The new framework consists of knowledge graph extended retrieval-augmented generation (RAG), generative AI amalgamation technology and the world's first generative AI auditing technology. The knowledge graph extended RAG uses knowledge graphs to link the relationships between large-scale data that companies possess and enhance the data input to the generative AI. The generative AI amalgamation technology selects the model with the highest performance from multiple specialized generative AI models based on the input task or it automatically generates by combining the models. The world's first generative AI auditing technology enables explainable output which will adhere to compliance with laws and company regulations.
Fujitsu is currently conducting a verification test using its generative AI framework for enterprises. It is expected to achieve a 30% reduction in manhours for contract compliance verifications, a 25% improvement in support desk work efficiency, and a 95% reduction in the time it takes to plan optimal driver allocation in the transportation industry.
