Defensible AI for Energy and Utilities
Energy and utility operators use AI for grid management, outage prediction, environmental compliance, and asset optimization, where regulators and the public scrutinize every decision. Seekr® gives operations, compliance, and engineering teams continuous observability, plain-language explainability, and a defensible audit trail for every decision.
How Seekr works for energy and utilities
From grid optimization through ongoing model monitoring, Seekr gives operations, safety, and compliance teams the evidence they need at every stage of the AI lifecycle.
Prepare and govern your data
Turn SCADA data, sensor feeds, maintenance logs, and environmental monitoring records into training-ready datasets with full lineage. Your data stays in your environment, and every preparation step is logged, so you can show regulators exactly what the model learned from.
Select and certify models
Compare candidate models side by side against your own operational scenarios, safety thresholds, and compliance criteria. Score for accuracy, safety margins, and failure modes before any model touches a production decision, so the model you approve is the one you can defend.
Deploy with full provenance
Every output carries a confidence score and a source trail that traces the answer back to the data, the reasoning, and the model version that produced it. When a utility commission or safety inspector asks how you reached an operational decision, the evidence is already assembled.
Monitor and re-evaluate
Detect model drift as demand patterns shift, generation mix changes, weather events intensify, or regulations tighten. Retest against the same criteria on a continuous basis, and maintain a documented record of performance, risk, and change over time.
Core capabilities
Seekr is AI you can defend to regulators: trace every output, test against your risk thresholds, correct errors on the record, and run inside your secured environment.
Model-agnostic explainability
Trace any output to the data and reasoning behind it. When regulators ask why a grid or maintenance decision was made, show exactly what influenced it.
Quantified model risk
Define risk profiles for your operational domains, safety thresholds, and regulations. Score models against them and catch failures before production.
Flexible deployment
Run in Seekr’s cloud, yours, on premises, or air gapped. Each is designed to keep critical infrastructure and SCADA data in your secured environment.
Continuous evaluation and drift detection
Demand shifts, generation mix evolves, and weather intensifies. Detect drift in accuracy, safety margins, and risk posture, with audit-ready evidence.
Contestability
When your team sees why a decision was made, they can challenge it, correct it, and confirm the fix, with every intervention documented.
Predictable cost structure
Match model and compute to each workload, with evidence to defend every choice. The leanest model that meets requirements keeps costs predictable.
Seekrflow is the AI operating system for energy and utilities
Launch prebuilt energy and utilities solutions instantly or create your own on a robust, extensible architecture built for scale. SeekrFlow enables full visibility and control over how AI learns, reasons, and delivers results in modern energy and utilities environments.
Deploy Seekr anywhere
Seekr cloud
Fully managed in the Seekr cloud. Fastest path to production for utilities and energy operators that want explainable AI before committing to on-premises infrastructure.
Fully managed in the Seekr cloud. Fastest path to production for utilities and energy operators that want explainable AI before committing to on-premises infrastructure.
Your cloud
Deploy in your AWS, Azure, or GCP environment. Operational data stays in your tenancy under your controls and your existing security posture.
Deploy in your AWS, Azure, or GCP environment. Operational data stays in your tenancy under your controls and your existing security posture.
On-premises
Deployment for operators that require physical control over SCADA networks, operational technology environments, or air-gapped infrastructure.
Deployment for operators that require physical control over SCADA networks, operational technology environments, or air-gapped infrastructure.
At the edge
Deploy at the substation, generation facility, or field operations center where latency or connectivity constraints require processing closer to the data source.
Deploy at the substation, generation facility, or field operations center where latency or connectivity constraints require processing closer to the data source.
FAQ
How does Seekr help energy and utility companies defend AI outputs?
Seekr traces every AI output to the data, reasoning, and model version that produced it, creating an evidence trail that maps to what regulators and commissions ask for. Instead of reconstructing why a grid optimization or maintenance decision was made after an incident or rate case, your team produces the provenance at the time of the decision. This covers load management, asset maintenance, environmental compliance, and any workflow where a regulator, commissioner, or safety inspector needs to see how the answer was reached.
Can Seekr run AI on critical infrastructure data without that data becoming compromised?
Yes. Seekr deploys in your cloud, your data center on premises, or air gapped. SCADA data, sensor feeds, and operational system information stay in your infrastructure, and your data is never used to train another organization’s models. You choose the deployment model, and Seekr runs where your data already lives.
How do we evaluate whether an AI model is safe to use for grid operations?
Seekr lets you compare candidate models side by side against your own data, scenarios, and safety thresholds rather than relying on generic benchmarks or vendor claims. You define what matters (accuracy under peak load conditions, safety margin preservation, performance under extreme weather scenarios) and score models against those criteria before any model reaches a production decision.
What happens when operating conditions change and a model has to change with it?
Seekr monitors for drift in accuracy, safety margins, and risk posture on an ongoing basis and flags when a model’s behavior deviates from the criteria it was approved against. You retest against the same framework you used during initial evaluation, which creates a documented record of performance over time. When conditions change, you re-evaluate, and the evidence record stays continuous.
How is Seekr different from observability tools we already use?
Observability tools record what happened: which data sources were queried, which algorithms ran, how long each step took. That’s useful for debugging. Seekr goes further by answering what influenced the outcome, tracing which inputs materially drove the output rather than logging system activity. In a rate case, a safety investigation, or an environmental compliance review, the difference between showing your workflow and showing your evidence is the difference between describing a process and defending a decision.
Make every operational decision one you can defend
See how Seekr works for energy and utilities with a customized briefing from one of our AI experts.
Request a Demo