#Japan #AI Data #R&D #Minato #IDX

AI Data Inc. Launches 'AI R&D on IDX' for Enhanced Research Efficiency

In a significant development for research and development (R&D) departments, AI Data Inc. (headquartered in Minato, Tokyo, led by CEO Takahito Sasaki) has introduced a new AI knowledge integration platform called "AI R&D on IDX." This platform promises to revolutionize the way researchers access and utilize various types of data, including literature, patent information, research notes, and experimental records.

Overcoming R&D Challenges

The current state of many research organizations often sees fragmented systems where paper databases, external patents, and internal experimental data exist in silos. This fragmentation often leads to substantial time wasted, as researchers are left struggling with questions like "What should I search for?" and "Am I repeating the same experiment?" Such issues contribute to what is commonly referred to as "R&D loss," particularly when research themes change or during personnel transfers, where knowledge transfer can become a significant bottleneck.

Recognizing the need for seamless integration, AI Data's "AI R&D on IDX" aims to eliminate these barriers to knowledge exploration. By utilizing its knowledge database and RAG (Retrieval-Augmented Generation) integration technology, the platform minimizes hallucinations and fosters accurate AI-generated responses tailored to practical applications.

Key Features of the New Platform

The newly launched system offers several innovative features designed to streamline the research process:

• - Unified Access: By combining literature, patents, and internal experimental data, researchers can pose a simple query and receive insightful results instantly.
• - Automated Tagging: The platform analyzes experimental reports and automatically tags conditions, objectives, and results, reducing manual workloads.
• - Similarity Detection: RAG technology accurately identifies similarities between past experiments, helping to enhance reproducibility and suggest possible improvements.
• - Visualization of Related Information: By visualizing relevant information per project, the system aids in the seamless transfer of research knowledge.
• - AI Assistant: Tailored AI assistants leverage accumulated databases to function as research advisors, providing targeted support.

Thanks to these advancements, researchers can expect to shift their time investment from 70% on searches and 30% on experiments to an impressive 20% on searches and 80% on experimentation. This transformation significantly enhances the speed of research development, reproducibility, and overall productivity.

Future Goals and Industry Applications

Looking forward, AI Data Inc. aims to implement this platform primarily in sectors that handle large volumes of experimental data, including pharmaceuticals, materials science, electronics, semiconductors, and food industries. The organization seeks to establish "AI R&D on IDX" as the de facto standard operating system for research and development.

About AI Data Inc.

Founded in April 2015, AI Data Inc. has been pivotal in protecting and maximizing the value of data assets for both organizations and individuals for over two decades. With a proven track record of earning trust from more than 10,000 corporations and over one million customers, the company offers comprehensive data ecosystem services that encompass sharing, backup, recovery, migration, and deletion capabilities. In addition, their cloud data management and recovery services have been awarded the top sales performance in the BCN Awards for 16 consecutive years.

Through its intellectual property management services and innovative platforms like 'Tokkyo.Ai,' AI Data supports the integration of data and IP for enhanced economic outcomes. They are also committed to developing young engineers in collaboration with the Ministry of Defense, reinforcing societal infrastructure through robust data management and IP protection.

In conclusion, AI Data Inc.'s "AI R&D on IDX" represents a significant leap forward in R&D efficiency and effectiveness, promising to reshape how researchers interact with and leverage their data.