01
Sensor anomaly to work order
An agent correlates a sensor deviation against maintenance history and equipment specs, then drafts a work order with the supporting evidence attached — not just a threshold alert.
Agentic AI in energy & utilities
Agents that work inside grid operations .
We build agents that read sensor and maintenance data, draft work orders, and retrieve engineering documentation during time-pressured operations — with the audit trail an operations or safety team will actually sign off on.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Most energy AI stops at a dashboard
A sensor anomaly that needs a work order, an outage that needs the right procedure fast, a maintenance backlog that needs prioritizing against real risk: grid and plant operations already produce these decisions under time pressure. Before an agent touches physical assets, an operations or safety team wants the escalation path, the audit log and the human approval gate already in place. That layer, rather than model quality, is what most energy deployments are missing.
every recommendation logged and reviewable
Use cases
Where agents earn trust in energy operations
Workflows with a clear decision boundary and a record already required by operations or safety practice.
02
Engineering document retrieval
During outage response, an agent pulls the relevant procedure or as-built drawing from thousands of engineering documents in seconds, sourced and citable, instead of a manual search.
03
Field service dispatch
A dispatch agent evaluates crew location, job priority, and equipment access to route the next field job — the same real-time evaluate-and-route pattern we have shipped in other high-throughput operations.
04
Maintenance backlog triage
An agent ranks open maintenance items against failure risk and asset criticality, drafting a prioritized schedule for an engineer to approve rather than a flat first-in-first-out queue.
What the mechanism delivers
The mechanism an outage workflow depends on, measured in production
None of these numbers come from an energy or utilities deployment — we have not shipped a production agent inside one. They come from Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare), and they are here because each measures something an outage-response or maintenance workflow already depends on: multi-step validation instead of a single generation pass, real-time evaluate-and-route across a distributed operation, and staged retrieval that stops rather than guesses where a wrong answer is a safety event. Every figure below links to the case study behind it.
Agents do not switch a feeder, move a setpoint, or close out a work order. They gather, cross-check and draft what sits before an operator's decision. Every step is logged for review.
Manual setup per multi-step workflow
2 hrs
Not an energy deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node.
95.4%
Success rate in workflow results
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
0 hallucinations
across 329+ nurse-validated scenarios
Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
Client results
Proof from production agentic deployments
We have not shipped a production agent inside an energy or utilities company. These are the closest references we have — the same multi-step validation, real-time routing, and staged retrieval under a safety constraint, shipped in engineering software, print on demand and healthcare.
Delivery path
From workflow audit to a production ops agent
TriStorm keeps operations and safety review aligned with engineering — risk surfaced before full build commitment.
Map workflow and constraints
We audit the target workflow, safety and access-control boundaries, and the systems it touches (SCADA, historian, EAM) ranking use cases by operational impact and integration risk.
- Workflow & risk audit
- System access map
- Prioritised use case
Build and validate the agent
We implement against real operational data shapes, with an evaluation suite scored against your own procedures before any recommendation reaches an operator.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout with monitoring, audit logging, and a structured handoff so your operations and engineering teams run the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own outage-response and maintenance workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in energy & utilities, answered
Where does agentic AI actually fit into an energy or utility operation?
In workflows where an operator today manually cross-references sensor data, work orders, and procedures under time pressure — outage triage, maintenance scheduling, engineering documentation lookup. We do not deploy agents to make unsupervised control-system decisions; we deploy them to gather, cross-check, and draft, with an operator or engineer as the final gate.
How is this different from the SCADA alerting and rules engines we already run?
A rules engine fires a fixed alert on a fixed threshold. An agent reasons across multiple sources (sensor history, maintenance logs, engineering documents, prior incidents) and drafts a next action, then escalates when its confidence drops. It extends what your control systems already flag; it does not replace them.
What energy-sector workflows have the clearest path to production?
Anything with a clear decision boundary and a record already required: work-order generation from sensor anomalies, engineering-document retrieval during outage response, field-service dispatch, and vegetation or asset-inspection triage. Each already carries an audit requirement, which is the structure an agent needs to work inside.
How do you handle grid-critical and safety-relevant systems?
We keep agents out of direct control-system actuation. They read from historians, SCADA, and asset-management systems through existing access controls, draft a recommendation or work order, and log every step. A human operator or engineer approves anything that touches physical assets or grid state.
What happens when the agent is not confident in its recommendation?
It escalates instead of guessing. Confidence thresholds and coverage gaps route to a human reviewer with the full reasoning chain attached, so the reviewer sees why the agent flagged it, not just that it did.
Can this integrate with our existing SCADA, EAM, and historian systems?
Yes, through your existing data infrastructure and APIs, not a rip-and-replace. Our text-to-workflow platform for Synera automates complex multi-step engineering and operational processes on top of existing systems, which is the same integration pattern we bring to energy operators.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real operational data, a working agent) typically lands in about three weeks. Full production rollout with monitoring and operator handoff follows the same TriStorm phases as any other Vstorm engagement.
Do you have energy-sector case studies?
Not yet inside an energy or utility company specifically. What we can point to is production agent work in comparably infrastructure-heavy, always-on operations — Synera's text-to-workflow platform (2 hrs → 3 min to generate a validated workflow) and Mixam's multi-agent system (95.4% success rate in workflow results) which evaluates and routes in real time across a distributed operation.
Start with one workflow
Map one operational workflow worth automating
A 30-minute call identifies the systems it touches, the escalation boundary, and a realistic path to a working agent your operations team will trust.