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AI Engineering Knowledge Assistant
Engineering knowledge sits across specifications, manuals, drawings, procedures, EAM systems, project folders and local repositories. Engineers spend significant time finding, validating and interpreting it and as experienced professionals retire, utilities risk losing the context needed to apply that knowledge correctly.
One governed knowledge layer can turn fragmented information into something teams can find, understand and reuse without disconnecting it from its source or controls.
Natural-language access surfaces approved standards, manuals, histories, procedures and lessons learned for the task at hand.
Previous projects, incidents and engineering lessons become easier to find and apply, reducing duplicated work.
Contextual access helps transfer accumulated experience to new engineers and technicians as seasoned professionals leave.
Permissions, version management, validation and human oversight help teams work from authorized, current information.
How NTT DATA helps
Bring authorized engineering and operational information together across document repositories, engineering platforms, EAM systems and other enterprise sources.
Combine generative AI, semantic search and Retrieval-Augmented Generation to identify relevant information and generate contextual responses based on approved content.
Surface maintenance history, previous failures, inspection procedures, standards and lessons learned around the specific asset or engineering problem.
Apply access controls, document permissions, version management and validation mechanisms so users receive authorized information with appropriate oversight.
We don’t ask AI to make engineering decisions. We keep human validation central where recommendations can affect infrastructure, safety and service reliability.
These are indicative improvement objectives the source notes that actual results depend on information quality, organizational maturity and deployment strategy.
Reduce search time by 30–50% by providing faster access to approved standards, manuals, procedures, asset history and lessons learned.
Reduce documentation preparation time by 20–40% by helping teams retrieve, compare and summarize relevant technical information more efficiently.
Increase engineering productivity by 15–30% by reducing time spent locating and interpreting information and enabling greater focus on technical analysis and judgment.
Reduce technical onboarding time by 20–40% by giving new engineers and technicians easier access to trusted procedures, historical knowledge and engineering context.
Turn technical knowledge into lasting utility capability
