AIGen for consultation and document generation

By combining LLMs, automated indexing and intelligent search, NTT DATA enabled faster access to critical information for nuclear plant life management and decision-making.
AI Gen
LLM Search
AI-powered search for complex technical content
Auto Indexing
Continuous onboarding of critical documentation
Faster Reports
Reduced time for life management reporting
Controlled Access
Authorized profiles for secure information usage
AI-powered consultation
AI-powered consultation and reporting for faster, smarter and more secure access to critical technical knowledge.
Generative AI simplified access to complex technical information.
目的
Improve reporting and critical information access.
機会
Use AI to accelerate document consultation and decision-making.
Complex Regulatory Knowledge Management
Critical technical information was fragmented across documents.
主な課題
• Defensible OT architecture and secure segmentation
Critical technical information was fragmented across documents.
Time-Intensive Information Search
Manual document reviews slowed analysis and decision-making.
Limited Access to Critical Insights
Users lacked efficient access to complex technical knowledge.
Title Growing Documentation Complexity
High-volume technical content made information retrieval difficult.
Continuous Knowledge Updating
New documentation required scalable and controlled onboarding.
解決策

Building an AI Platform for Intelligent Consultation and Reporting

チェックアイコン
LLM-Powered Search

Enabled intelligent consultation across technical documents.

チェックアイコン
Technical Document Indexing

Indexed complex documentation for faster information access.

チェックアイコン
AI Consultation Interface

Created a front-end for efficient user interaction.

チェックアイコン
Secure User Governance

Defined controlled access through authorized user profiles.

影響
Accelerating Critical Decision-Making

The AI platform reduced reporting effort, improved access to critical information and enabled scalable knowledge management for future AI use cases.

ドラッグ