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LangGenius Inc., developer of AI platform "Dify," exhibited at Japan DX Week.

NQ Score 75/100
N1 Content Completeness 8

AI Summary (NQ-processed)

LangGenius Inc., headquartered in Tokyo, exhibited its AI platform "Dify" at the Japan DX Week "AI & Business Automation Expo" held at Tokyo Big Sight from April 8-10, 2026. The company demonstrated Dify's AI workflow, RAG pipeline, and no-code/low-code AI agent building capabilities, including enterprise features like on-premise deployment and flexible RAG settings. LangGenius also reported on key AI challenges faced by Japanese enterprises, such as secure data handling, internal deployment, and RAG accuracy, which Dify aims to solve. Major clients like NTT and Mizuho Bank were mentioned.

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Frequently Asked Questions

Q: What is LangGenius Inc. and what product did they showcase at Japan DX Week?
A: LangGenius Inc. is the developer of the AI platform "Dify," which they exhibited at the Japan DX Week "AI & Business Automation Expo."
Q: When and where did LangGenius Inc. exhibit their Dify platform?
A: LangGenius Inc. exhibited at the Japan DX Week "AI & Business Automation Expo" from April 8 to 10, 2026, at Tokyo Big Sight.
Q: What specific features of Dify were highlighted during the exhibition?
A: The exhibition highlighted Dify's AI Workflow & Knowledge Pipeline (RAG pipeline), its visual workflow builder for no-code/low-code AI agent construction, flexible chunking strategies, reranking settings, multi-model support, and external system integration.
Q: What were the three primary challenges Japanese companies face in AI adoption, according to LangGenius?
A: The three primary challenges identified were the need for secure AI utilization, difficulties in internal deployment and maintenance of AI tools, and concerns regarding RAG accuracy and hallucination.
Q: How does Dify address the identified challenges for Japanese companies?
A: Dify addresses secure AI utilization with on-premise and private cloud options, simplifies deployment and maintenance through its no-code/low-code approach, and mitigates RAG accuracy concerns with its flexible knowledge pipeline configuration.