Do we need to replace our SCADA or monitoring systems?

No. We build above the systems you already run, reading data through their interfaces and writing back only where you approve, such as creating work orders.

Where does AI pay off first in energy operations?

Usually underperformance detection and alarm triage. Both use data you already collect and shorten the time between a loss starting and someone fixing it.

Does this work for oil and gas as well as renewables?

Yes. The same pattern applies to upstream and midstream assets: unify sensor and maintenance data, start with anomaly alerts, then move to prediction.

How do you keep AI agents safe around critical infrastructure?

Agents recommend and draft; engineers approve. Access is read-only by default, every action is logged, and nothing is changed in control systems without explicit approval.

How long until we see results?

A performance dashboard and alerting for one or two sites typically goes live in about two months, before expanding across the fleet.

Why Sofyrus instead of a large consultancy?

We are a product engineering firm that builds and runs operational AI with our energy partner DomainXeed. You get working software in weeks at a predictable cost.

ENERGY, RENEWABLES & OIL AND GAS

More assets, more data, same team. AI closes the gap.

Sofyrus builds governed AI agents and operational software that sit above your SCADA, maintenance and ERP systems, turning plant data into faster fixes and fewer losses.

Discuss Your Energy Project See the Inergio Work

76%

of power and renewables executives plan to increase AI spending (Deloitte, 2026)

250+

software projects shipped

14+ years

of product engineering

WHAT'S CHANGING

Energy now

AI is both a growth driver and a new source of demand for the energy system. Sources: Deloitte 2026 Renewable Energy Industry Outlook, Deloitte AI infrastructure research (2025), ADIPEC 2026 energy survey.

76%

of power and renewables executives plan to increase AI spending (Deloitte, 2026)

30x

possible growth in AI data center power demand by 2035 (Deloitte)

81%

of energy decision-makers say current forecasts underestimate AI power demand (ADIPEC, 2026)

(PERSPECTIVES)

Five AI moves for energy operators

WHAT WE BUILD

AI-first energy products we build

Working software in weeks, layered above the systems your operation already runs.

Performance and alerting platform

Expected-versus-actual models with automatic underperformance alerts across sites. Moves energy losses and response time.

AIMonitoring

Operations agent layer

Governed AI agents for alarm triage, reporting and work orders, with human approval. Moves engineer hours per incident.

Agentic AIOperations

Predictive maintenance

Failure prediction for inverters, trackers and rotating equipment. Moves unplanned downtime.

PredictionMaintenance

Image inspection

Drone and thermal image analysis that turns findings into work orders. Moves time to find module-level faults.

Computer visionInspection

Energy data platform

Unified asset model, pipelines and KPIs across mixed fleets. Moves time to launch each new use case.

DataIntegration

Field and reporting apps

Mobile apps for technicians and automated reports for owners, lenders and offtakers. Moves reporting effort.

MobileReporting

01 — IS THIS YOUR OPERATION?

01 / 03

More assets. More data. The same manual reporting.

For renewable energy developers and operators, and oil and gas companies, whose teams spend too much time moving data between systems and compiling reports. It fits operators who want AI on top of existing systems, not a risky platform replacement.

Fragmented systems

Operations, maintenance, and finance data live in separate tools.

Manual reporting

Engineers compile performance reports instead of acting on them.

Slow response

Issues are found late because no one sees the whole picture in time.

02 — HOW IT WORKS

02 / 03

A governed AI layer above existing systems.

Agents read from your systems through agreed access, prepare insights and actions, and leave high-stakes decisions to your people.

Read, don't replace

Agents connect to existing systems instead of replacing them.

Human approval

Operational actions are reviewed and approved by named staff.

Auditable

Every agent output and action is logged for review.

03 — DELIVERED WITH DOMAINXEED

03 / 03

Inergio: a multi-agent operating layer for solar.

DomainXeed is our client and partner for energy solutions in renewables and oil and gas. Together we built Inergio, a governed multi-agent layer for solar operations.

Discuss your energy project

Operations, insight & engineering copilots

Specialized agents working above existing solar operations systems.

Robonito-led QA

Automated testing of the platform with Robonito, our AI test-automation product.

Engagement targets

30% process-efficiency uplift and up to 40% operational-cost reduction.

SOFYRUS DELIVERY FRAMEWORK

From one site to the whole portfolio.

Prove value on one workflow and site, then scale with evidence.

Scroll to follow the plan

Delivery phase

01 / Discover

Map one operational workflow, its systems, data sources, and the decisions it supports.

Phase 1 of 6

Delivery phase

02 / Design

Define agent responsibilities, data access, approval steps, and the reports and alerts teams need.

Phase 2 of 6

Delivery phase

03 / Connect & Build

Connect to agreed data sources and build agents and dashboards in increments, starting with test data.

Phase 3 of 6

Delivery phase

04 / Evaluate

Test accuracy, alert quality, failure handling, and effort saved against agreed thresholds.

Phase 4 of 6

Delivery phase

05 / Launch

Go live on one site or asset group with monitoring, named owners, and a rollback plan.

Phase 5 of 6

Delivery phase

06 / Operate & Scale

Expand to more sites and workflows once results meet the agreed targets.

Phase 6 of 6

Delivery phase

01 / Discover

Map one operational workflow, its systems, data sources, and the decisions it supports.

Phase 1 of 6

Delivery phase

02 / Design

Define agent responsibilities, data access, approval steps, and the reports and alerts teams need.

Phase 2 of 6

Delivery phase

03 / Connect & Build

Connect to agreed data sources and build agents and dashboards in increments, starting with test data.

Phase 3 of 6

Delivery phase

04 / Evaluate

Test accuracy, alert quality, failure handling, and effort saved against agreed thresholds.

Phase 4 of 6

Delivery phase

05 / Launch

Go live on one site or asset group with monitoring, named owners, and a rollback plan.

Phase 5 of 6

Delivery phase

06 / Operate & Scale

Expand to more sites and workflows once results meet the agreed targets.

Phase 6 of 6

RELEASE CRITERIA

Four practical questions guide the release decision.

Agree what success means before we build.

Measure

Data accuracy

Evidence to review

Agent outputs match source data and engineering review in sampled checks.

Measure

Safe actions

Evidence to review

No operational action is taken without the agreed approval.

Measure

Measurable impact

Evidence to review

Time saved, issues caught, and cost impact compared against the baseline.

Note

Targets are agreed with your team and checked against your current baseline.

A CONCRETE WAY TO START

Choose the engagement that matches where your product is today.

Three ways to start.

Most operators start with an Operations Assessment, then pilot on one site before scaling.

DEFINE THE OPPORTUNITY

Energy Operations Assessment

Workflow and data map, integration review, use-case priorities, and a pilot proposal. Written scope and deliverables Recommendation to proceed, revise, or stop

Request an assessment

PROVE THE VALUE