An AI-powered engineering knowledge model for asset integrity and maintenance
Supporting the development of an AI-driven engineering knowledge platform for operational excellence.
Asset One supported Open Innovation in the development of a Large Language Model (LLM) for ADNOC Gas, designed to improve access to engineering knowledge relating to asset integrity, maintenance and operational learning.
The project combined engineering expertise with artificial intelligence to help transform decades of technical information into an intelligent knowledge platform capable of supporting engineers with faster access to operational experience, equipment failures and HSE learning.
Engagement overview
ADNOC Gas
Open Innovation
Subject Matter Expert (Asset Integrity & Maintenance)
United Arab Emirates
Oil & Gas
Asset Management, Equipment Management, Maintenance Management, Knowledge Management, AI-enabled Engineering, Operational Excellence
What needed to be addressed
Large operating organisations generate significant volumes of engineering knowledge over many years, including equipment failure investigations, maintenance history, HSE incidents, technical reports and lessons learned.
While this information is valuable, it is often distributed across multiple systems and documents, making it difficult for engineers to quickly locate relevant experience when making operational or maintenance decisions.
ADNOC Gas wanted to explore how Artificial Intelligence could improve access to this knowledge while supporting safer operations, improved reliability and more informed engineering decision-making.
How we approached the work
Asset One worked as part of the wider project team led by Open Innovation, providing practical engineering expertise across asset integrity and maintenance disciplines.
Our role focused on ensuring the engineering knowledge presented to the AI model reflected real operational practice and recognised industry asset management principles. Support included:
- Asset integrity subject matter expertise
- Maintenance engineering expertise
- Equipment failure knowledge
- Engineering terminology and context
- Asset management best practice
- Maintenance strategy concepts
- Failure mechanisms and operational learning
- Engineering review and validation
- Knowledge structuring for AI interpretation
- Supporting engineering-focused prompt development
Defined deliverables
Equipment failures
Historical equipment failure investigations and findings made accessible to engineers.
Maintenance history
Records of maintenance activity, strategy and performance available for interrogation.
HSE incidents
HSE event records, causes and lessons learned captured within the model.
Lessons learned
Operational and technical lessons captured over time, structured for AI interpretation.
Engineering reports
Technical reports and engineering documentation brought into a searchable knowledge base.
Operational experience
Practical operational experience and field knowledge made available to engineering teams.
Asset integrity knowledge
Integrity management principles, failure mechanisms and technical barrier understanding.
Maintenance best practice
Recognised maintenance strategy and reliability practice reflected in the knowledge model.
Connected system
The project contributed to the development of an AI-powered Large Language Model capable of helping engineers interrogate extensive engineering knowledge — making it easier to access and more useful during operational decision-making, and helping engineers identify relevant historical information significantly faster than traditional document searching. Rather than developing AI technology itself, Asset One contributed the engineering, maintenance and asset management expertise necessary to ensure the solution reflected real-world operational practice and engineering decision-making — bridging the gap between engineering knowledge and digital innovation.
What the work achieved
The solution supports ADNOC Gas's wider ambition of achieving operational excellence by improving the accessibility and practical use of engineering knowledge. Potential benefits include:
- 01Faster access to engineering knowledge
- 02Improved organisational learning
- 03Better understanding of historical equipment failures
- 04Increased visibility of HSE learning
- 05Improved engineering decision support
- 06More consistent knowledge sharing
- 07Enhanced asset integrity awareness
- 08Better support for maintenance planning
- 09Greater utilisation of existing engineering information
- 10Supporting digital transformation initiatives
Looking to combine engineering expertise with digital innovation?
Asset One helps organisations improve asset performance by combining practical engineering experience with modern digital technologies, ensuring AI and engineering work together to deliver better operational outcomes.