Palo Alto, California SymphonyAI, the global leader in vertical AI platforms, announced eight new industrial AI applications built specifically for energy utilities. This represents IRIS Foundry’s most targeted expansion into the energy sector to date.
Unlike typical asset management software, these applications are designed around specific failure modes, process dynamics, and regulatory obligations of energy and resource operations, such as compressor surges, fouled heat exchangers, compromised pipeline integrity, refinery yield losses, and increased compliance burdens due to EU methane regulations and emissions reporting. Combining SymphonyAI’s deep industrial ontology with IRIS Foundry’s ability to integrate IT, OT, and IoT data from historians, SCADA systems, inspection databases, and enterprise platforms into a single managed intelligence layer, the new suite delivers causal AI at the point where energy utilities lose maximum uptime, margin, and safety headroom.
8 new applications for energy operations
- Rotating equipment health and failure prediction: Agent AI for continuous monitoring of the health of compressors, pumps, turbines, and motors across energy operations. Deploy specialized agents for anomaly detection, remaining useful life modeling, and maintenance workflow automation to predict failures up to 30 days in advance and trigger work orders before unplanned shutdowns occur.
- Asset integrity and inspection intelligence: AI-powered integrity management of pressure vessels, piping, storage tanks, and structural components. Combine inspection history, corrosion modeling, and process condition data with a risk-based inspection framework to prioritize inspection workloads, predict deterioration rates, safely extend run durations, and replace calendar-based schedules with condition-driven intelligence compliant with API 580/581.
- Heat exchanger network dirt monitor: Real-time fouling detection and cleaning schedule optimization for heat exchanger networks in refineries and gas processing plants. Model heat transfer degradation to baseline performance, predict time-to-clean thresholds, and optimize cleaning events based on production planning. This reduces energy waste, increases uptime and prevents process disruption due to contamination.
- Refinery yield and margin optimization tools: Ensemble AI delivers real-time crude slate optimization, unit yield modeling, and margin maximization across distillation, cracking, and processing units. Override capabilities and a full audit trail for all AI-generated decisions provide transparent, operator-ready recommendations with full model interpretability. That is, it not only tells you what to change, but also why you want to change it.
- Real-time operations center and P&ID intelligence: A unified operations monitoring platform that combines live SCADA/DCS data with interactive P&ID overlays, AI-generated alarm streamlining, and integrated operations assistants. Operators see real-time process conditions directly on engineering drawings, receive contextual guidance on deviations, and access remote expert support, reducing response time to process anomalies and eliminating context switching between HMI screens and documents.
- Turnaround and outage planning intelligence: AI-driven planning and execution management for planned turnarounds, shutdowns, and outages. Integrate scope of work, inspection results, critical path schedules, contractor management, and material availability to accelerate delivery times, control cost overruns, and ensure safe return to service. Address the costliest and riskiest planned events in your energy operations.
- Flare and leakage release intelligence: Reduce flare events, fugitive methane emissions, and VOC emissions across production, processing, and refining operations using real-time monitoring and AI. Automated reporting aligned to EU ETS, EU Methane Regulation, and IED requirements to detect abnormal flaring conditions, identify root cause, and recommend operational changes to minimize environmental and regulatory impact.
- Pipeline integrity and leak detection: Combining flow balancing, acoustic sensing data, and inline inspection records, AI continuously monitors pipeline networks for leak detection, corrosion progression, and pressure anomalies. Identify anomalies within meters, distinguish product loss from measurement noise, and integrate with GIS mapping to guide rapid field response across collection lines, transmission pipelines, and distribution networks.
Why this matters: Energy facilities operate at different levels of asset impact.
In the energy industry, the impact of asset failure is critically different than in most industrial environments. A failed compressor on a gas processing platform, a fouled heat exchanger network in a refinery, an undetected leak in a pipeline, or an unplanned repair extension each have safety, environmental, and financial impacts that require a level of predictive intelligence that typical industrial AI cannot provide. These applications were built based on that reality.
In energy operations, process conditions and asset health are inseparable. Compressors handling richer gas compositions, heat exchangers handling heavier crude oils, pipelines operating at higher pressures during peak demand, etc. each legitimately change the behavior and probability of failure of an asset. Common predictive maintenance tools trained on manufacturing data cannot interpret these relationships. IRIS Foundry’s industrial ontology understands the physics of energy operations, allowing platforms to separate actual degradation from normal operational fluctuations and directly manage maintenance resources to prevent the most severe failures.
Built for energy data complexity
Energy facilities generate asset and process data across fundamentally incompatible systems, including OSIsoft PI Historians, SCADA platforms, test management databases, maintenance systems, laboratory information systems, and enterprise ERP platforms. IRIS Foundry consolidates these data streams into a single, managed intelligence layer without requiring operators to replace their existing infrastructure. Applications are deployed on top of this integrated foundation, combining real-time sensor intelligence with asset history, inspection records, and operational context to generate insights that can be executed simultaneously in the control room and reported to boards and regulators.
The result is an operational intelligence capability that scales from a single refinery unit to a global portfolio of multiple locations, adapts to existing infrastructure, and delivers measurable intelligence returns in weeks instead of months.
Built for production on Microsoft Azure
Developed using IRIS Forge, SymphonyAI’s AI-based code generation solution, these applications integrate Microsoft Foundry, Azure Kubernetes Service (AKS), Azure Edge Runtime, and more to address the most valuable bottlenecks across energy and resource operations.
Applications built on Azure for speed, scale, and security to process the large amounts of data generated by energy facilities leverage the robust Azure native architecture.
- Real-time intelligence: Leveraging Azure IoT Operations, applications process critical data close to the source, enabling low-latency decisions that are essential for making critical real-time decisions for continuous processes.
- Enterprise-grade scalability: Built on Azure Kubernetes Service (AKS) and Azure Data Lake, the suite is highly available and scales from a single unit to a multi-site global deployment.
- Uncompromising security: The platform leverages Microsoft Entra and Azure Key Vault to ensure the safety of sensitive proprietary manufacturing formulas and operational data.
Beyond operational data, IRIS Foundry integrates with Microsoft Teams and Microsoft 365 Copilot via Model Context Protocol (MCP). This integration brings Live Industrial co-pilot to life within Teams, allowing factory managers and operators to query production status, receive alerts on anomalies, and collaborate on root cause analysis without leaving the collaboration platform, democratizing access to high-value industrial insights.
Management perspective
“Energy utilities manage some of the most critical assets in the industry – equipment where a missed fault signal not only means lost production, but also safety events, environmental incidents, regulatory actions, etc. These applications were designed with that level of risk in mind. Enel It combines a deep domain model of how assets actually degrade with the causal inference needed to distinguish real warnings from noise, and does it in the operational context of each specific asset, rather than against a general baseline. Built by AI” the way each asset-intensive industry needs.” — Prateek Kathpal, President of SymphonyAI Industrial
“The energy transition is accelerating the data challenge for energy utilities. They are simultaneously managing traditional infrastructure and new digital assets under stricter emissions regulations and with a slimmer workforce. IRIS Foundry provides operators with a layer of intelligence that connects these worlds, from the sensors on a 30-year-old compressor to the emissions report on a CEO’s desk. And it does it without having to tear down and replace the infrastructure they’ve spent decades building.” — Kumar Abhimanyu, Senior Vice President of Strategic Partnerships, SymphonyAI
“Energy utilities manage assets where reliability, safety, and emissions performance are inseparable, and general-purpose AI just doesn’t meet those criteria,” said Darryl Willis, corporate vice president, Energy & Resources Industries, Microsoft. SymphonyAI’s purpose-built applications on the Microsoft platform help energy utilities transform complex operational data into actionable intelligence to improve asset reliability, operational performance, and regulatory readiness at scale. ”
