From fragmented manuals and complex plant systems to an intelligent, conversational knowledge assistant that delivers source-verified answers in seconds.
Overview
Field Engineers in Oil & Gas facilities are the first line of response when equipment issues arise, yet they often lose critical time not fixing problems, but finding the information needed to fix them.
This solution deploys an Agentic AI Assistant that unifies:
- Structured plant data (BOMs, Equipment Registers)
- Unstructured technical documentation (O&M manuals, GA drawings, P&IDs)
into a single conversational interface.
Built on a multi-agent RAG architecture orchestrated through Dataiku, the assistant lets engineers ask plain-language questions and receive validated, citation-backed answers in seconds—turning hours of manual document search into an instant, trusted response at the point of need.
Challenges
Critical Knowledge Management Challenges
Field Engineers in Oil & Gas plants face critical knowledge management challenges that impact operational efficiency and safety.
1.Information Overload
Thousands of O&M manuals, GA drawings, P&IDs, BOMs, and equipment specification PDFs exist across disparate repositories making manual search nearly impossible.
2.Complex Knowledge Systems
Structured data (BOMs, Equipment Registers) and unstructured documents (PDFs, manuals) are disconnected, leaving no unified query layer.
3.Slow Troubleshooting
Engineers spend 40–60% of their time locating the right document, cross-referencing part numbers, and validating equipment compatibility manually.
Solution
Agentic RAG-Powered Knowledge Retrieval System
An Agentic RAG-powered knowledge retrieval system for Field Engineers, using:
- Graph Search
- Vector RAG
- Structured RAG agents
All orchestrated on Dataiku.
The platform combines structured enterprise data with unstructured technical documents, enabling engineers to retrieve contextual, source-backed answers through natural language conversations.
Impact Created
- 70% Reduction in Knowledge Search Time
Engineers spend significantly less time locating manuals, drawings, and specifications. - 3× Faster Equipment Troubleshooting
Multi-agent retrieval dramatically accelerates troubleshooting workflows. - Operational Efficiency
Field Engineers can retrieve SOPs, part numbers, compatibility data, and revision history through natural language without SQL queries or manual document searches. - Knowledge Democratization
Every engineer gets equal access to the complete enterprise knowledge base from O&M manuals to GA drawings and certified P&IDs. - Reduced Human Error
AI-validated citations and source references reduce the risk of acting on outdated or incorrect procedures. - Accelerated Decision Making
Multi-agent orchestration retrieves answers from structured and unstructured systems simultaneously, delivering complete answers in seconds instead of hours.
Transformation Snapshot
| Feature | Before | After |
| Document & Knowledge Search | Engineers manually searched across disconnected PDF repositories, shared drives, and legacy systems to locate the right O&M manual, drawing, or specification sheet. | A single natural language query returns unified answers pulled simultaneously from structured and unstructured sources. |
| Troubleshooting Time | 40–60% of an engineer's time was spent locating documents and cross-referencing information before actual troubleshooting could begin. | Answers are retrieved in seconds through multi-agent orchestration, enabling 3× faster equipment troubleshooting. |
| Access to Knowledge | Deep plant knowledge was concentrated with a handful of senior or tenured engineers, creating bottlenecks and single points of failure. | Every field engineer has equal, on-demand access to the full enterprise knowledge base—from O&M manuals to certified P&IDs. |
| Accuracy & Reliability | Part numbers and equipment compatibility were cross-referenced manually, with a high risk of human error or reliance on outdated versions. | AI-validated responses include source document citations, reducing the risk of engineers acting on incorrect or outdated procedures. |
| Decision Making | Troubleshooting was reactive and delayed, often requiring escalation or waiting for documentation to be located. | Engineers receive complete, cross-referenced answers in seconds, enabling faster and more confident decisions in the field. |
Conclusion
By bridging the gap between siloed structured data and scattered technical documentation, this Agentic AI Assistant transforms how Field Engineers access critical operational knowledge replacing hours of manual searching with seconds of intelligent, source-verified retrieval.
The result isn't just faster troubleshooting; it's a more resilient, consistent, and safer operation, where every engineer regardless of tenure can act with the full weight of the plant's institutional knowledge behind them.
"What used to take an engineer an hour of digging through manuals now takes seconds with the confidence of knowing exactly where the answer came from."
