Operational and data constraints that slow AI adoption in Oil & Gas
Production-Critical Environments
AI must operate reliably alongside PI System, historian exports, and operational workflows where downtime directly impacts throughput and safety
Restricted Operational Data
Seismic surveys, equipment performance data, and operational logs cannot exit refinery or field network boundaries
Audit-Bound Workflows
AI outputs must preserve traceability and align with established maintenance data structures (e.g., ISO 14224) to support HSE and environmental reportin
Controlled AI access architecture for industrial energy systems
AI Gateway for secure access to engineering and operational data
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- A single interface for LLMs working over PI System exports, SAP PM data and technical documentation, without ever moving data outside controlled networks
- Ensure AI integrates consistently into engineering and production-support workflows while maintaining strict access boundaries and audit logging
AI deployments built for restricted Oil & Gas environments
- Deploy in VPC, private cloud, or on-prem refinery/field clusters to keep operational and engineering data within the same security perimeter as existing systems
- Maintain full traceability of prompts, outputs, model versions, and approvals to support HSE documentation and maintenance-governance needs
Governed agent execution for engineering and maintenance intelligence
- Use TrueFoundry’s MCP Gateway to centrally register agents and enforce discipline-specific access to SAP PM extracts, inspection reports, alarm/event logs, etc
- Apply routing, caching, and rollout governance so troubleshooting assistants, maintenance-analysis agents, and shift-handover summaries move safely from pilot to production
Operationalize AI agents for field, plant, and engineering teams
Shift handover assistant
Inspection summary copilot
Root-cause analysis agent
Asset monitoring agent
Production reporting agent
Seismic data analysis agent
TrueFoundry has helped us speed up the rollout of LLM capabilities while reducing the engineering burden on our teams. We can deploy and refine new models quickly, and their support operates like an extension of our own product organization.
GenAI infra- simple, faster, cheaper
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