Put AI to work without giving up operational control.
Energy organizations run on assets, data and the experience of the people who maintain them.
Margins builds AI into energy workflows to help teams make sense of inspection reports and sensor data, prepare field work faster, surface what deserves attention and keep engineering knowledge accessible — while existing control systems and qualified people stay in charge of operations.
More insight. Less manual effort. Operators stay in control.
Energy already runs on data. AI changes how much of it people can actually use.
Around control systems, assets and networks sits enormous amounts of work involving people:
- Inspecting
- Reading
- Diagnosing
- Searching
- Forecasting
- Planning
- Scheduling
- Dispatching
- Documenting
- Reporting
AI is most valuable when it expands human capacity without taking operational responsibility away from people.
Some of the most important asset information doesn’t live in a database field.
- Inspection reports
- Maintenance logs
- Shift notes
- Drone and site photos
- Incident reports
- Engineering drawings
- Equipment manuals
- Permits
- Regulatory filings
- Contractor reports
- PDFs
- Spreadsheets
- Emails
- 01Unstructured information
- 02Understand
- 03Extract
- 04Link to asset
- 05Validate
- 06Asset record
- 07Engineer review
Turn what inspectors write down into information maintenance systems can work with.
Not every step requires an engineer.
Most energy workflows contain both. AI and automation can take on the repetitive information work around maintenance, field service and reporting while keeping people focused on the parts that need expertise.
Preparation.
- Collect readings.
- Check report fields.
- Extract findings.
- Match to assets.
- Apply known thresholds.
- Draft work orders.
- Prepare reports.
Expertise.
- Interpret ambiguous findings.
- Assess asset condition.
- Approve switching and outages.
- Make safety-critical decisions.
Automate the preparation. Keep engineering judgment where safety depends on it.
Let engineers work the exceptions instead of reviewing every finding equally.
Inspection programs and alarm streams often require a person to look at every item. AI can read findings, apply approved criteria and flag uncertainty — routing routine items into planned maintenance and anomalies to the engineers who know the asset.
The objective isn’t maximum automation. It’s putting expert attention on the assets that need it.
The answer shouldn’t depend on finding the engineer who remembers the procedure.
Don’t make field crews search the document system. Let the document system answer them.
Explore Enterprise Knowledge & RAG- “Which procedure applies to this transformer type?”
- “What isolation steps does this job require?”
- “What changed in the latest revision of this standard?”
- “When was this asset last inspected, and what was found?”
- “Show me the source for this answer.”
Some of your most valuable operational knowledge is about to retire.
- 01
A senior lineworker knows which feeder misbehaves in heavy rain.
- 02
A plant operator hears that a pump sounds wrong before any alarm fires.
- 03
A protection engineer knows why a setting differs from the standard.
- 04
A dispatcher knows which crew can handle which site safely.
- 05
A maintenance lead knows why the documented interval works differently in reality.
Procedures explain how the network should be run. Experienced people understand how it actually behaves. AI becomes more useful when it can work with both.
Explore CaptaCritical infrastructure intelligence shouldn’t require giving up control of your data.
Useful energy AI may need access to- Asset registers
- Network topology
- Operational data
- Inspection records
- Customer and billing data
- Site security information
- Engineering knowledge
- Trading positions
- Incident history
- 01Customer-controlled cloud
- 02Private infrastructure
- 03On-premise deployment
- 04Segregated IT / OT boundaries
- 05Open-source models
- 06Selective external models
- 07Hybrid architectures
Critical-infrastructure requirements such as NIS2 shape the architecture. Decide intentionally what stays inside, what can leave and why.
AI should fit into the operational architecture, not sit beside it.
Relevant information may live across SCADA and historians, EAM and CMMS, GIS, outage management, ERP, billing and customer systems, document management, forecasting tools and field service applications.
The goal isn’t another platform crews need to open. It’s intelligence inside the systems and processes they already use — read access first, control systems untouched.
Explore IntegrationsStart with the workflow where expert time is spent preparing, not deciding.
Look for processes where:
- Engineers read large volumes of inspection and test reports.
- The same condition checks happen repeatedly.
- Work orders move through several systems before a crew is sent.
- Experts spend time assembling context rather than deciding.
- Alarms and anomalies are hard to separate from noise.
- Crews repeatedly search for procedures and asset history.
- Knowledge sits with people close to retirement.
- Manual reporting creates operational bottlenecks.
- 01Operational problem
- 02Process
- 03Assets + data + systems
- 04Safety / security requirements
- 05AI opportunity
- 06Value × Feasibility
- 07First implementation
The best AI opportunity is where automation creates value without weakening the safety and control the operation requires.
Start controlled. Expand with evidence.
Energy AI doesn’t need to begin anywhere near the control room.
Search
Find procedures and asset history faster.
Assist
Prepare work packs and reports.
Analyze
Detect anomalies and asset risk.
Recommend
Support maintenance and planning decisions.
Automate
Handle defined back-office workflows.
Agentic
Allow approved actions in business systems.
Increase autonomy as confidence, controls and evidence increase — never past the operator.
Build the intelligence and the controls around it.
Operations first
Start with the reliability, safety or service outcome rather than the model.
AI + software engineering
Build complete production systems rather than isolated AI demonstrations.
Private architecture
Design around critical-infrastructure data and customer-controlled environments.
Enterprise integration
Connect AI to asset, network, field and customer systems without disturbing control systems.
Multiple AI approaches
Use LLMs, RAG, time-series ML, forecasting and automation according to the problem.
Production ownership
Forward Deployed Engineering and Managed AI keep the system close to operational reality after deployment.
Related capabilities
Intelligent Automation
Move inspection, work order and reporting workflows toward exception-based operations.
Explore Intelligent Automation →Enterprise Knowledge & RAG
Give engineers and field crews trusted access to procedures and asset history.
Explore Enterprise Knowledge →Private & On-Premise AI
Build AI around critical-infrastructure data without unnecessarily giving up control.
Explore Private AI →AI Agents
Introduce controlled AI actions into business workflows, with people approving what matters.
Explore AI Agents →Managed AI
Operate and monitor AI after it enters production.
Explore Managed AI →
Where are your engineers spending more time preparing than deciding?
We’ll work with your operations, engineering and technology teams to identify where AI can materially reduce repetitive work, improve access to asset and procedural knowledge or strengthen planning — while designing around the safety and security requirements of critical infrastructure.
Explore AI opportunitiesStart with the workflow. Define the controls. Prove the value.