Start from an operating question
Start from a plant, alarm, device, report, or knowledge question without worrying about the underlying implementation.
AI Capability
AI operating capabilities and the expert report team
Scenario-ready AI capabilities, a multi-role virtual operations analysis team, and auditable outputs grounded in real asset data.
Read trusted data
The system reads monitoring data only within existing account and permission boundaries, grounding answers in real assets.
Return evidence outputs
Judgment, charts, source values, and next steps are organized into outputs the business can review directly.
Enter delivery review
Before rollout, layout, evidence sources, missing-data explanations, and scenario rules are checked to avoid unacceptable output.
Deliverable outputs
You do not need to understand how the system is decomposed. You need four stable outcomes.
ZenovaOS AI packages data retrieval, evidence organization, and delivery review inside the product, and delivers results that can move directly into operations.
01
Conclusion-first answers
Lead with the judgment, then show evidence, impact scope, and next steps for fast daily decisions.
02
Chart evidence
Render trends, comparisons, rankings, and anomaly dates as reviewable charts instead of asking teams to read raw tables.
03
Operating reports
Turn multi-step analysis into weekly reports, monthly reviews, fault reviews, and executive summaries.
04
Action lists
Provide priority, owner guidance, review checkpoints, and follow-up items so teams can close the loop.
Operating proof
Built for monitored assets, not generic Q&A.
ZenovaOS AI treats the existing monitoring platform as the source of truth. The AI capability can reason across live data, historical evidence, chart evidence, and report workflows while respecting account boundaries.
Route user intent into data-backed plant and device analysis.
Retrieve compact historical analysis results when prior evidence is more useful than another data pull.
Escalate deep report work to a multi-role report team when a management-ready report is the right output.
Explain verified unavailable-data reasons instead of filling gaps with placeholders or invented formulas.
Expert report team
A virtual operations analysis team, not a chat box.
Five roles work like an operations analysis team: collect the data, diagnose, interpret for the business, recommend actions, then a quality validator signs off — producing a reviewable, consulting-grade report.
01
Data collection
Pulls plant, device, alarm, and generation data scoped to the report goal.
02
Diagnostic analysis
Forms hypotheses and verifies them with data to locate losses and root causes.
03
Business interpretation
Translates technical findings into language management can read.
04
Action recommendation
Produces a prioritized, followable action list.
05
Quality validation
Cross-checks numbers, charts, and conclusions — substandard drafts go back.
Where it is used
Monthly operations review
Conclusion-first monthly materials, ready before the meeting.
Fault review
Fault timeline, impact, and remediation organized into a review report.
PR analysis
Explains whether PR swings come from weather, devices, or O&M actions.
Post-investment management
Asset performance and loss attribution for asset owners.
Operating review
Verification and review materials at weeks 2, 4, and 8 of a pilot.
How it differs
How is this different from monitoring platforms and generic AI chat?
Monitoring platforms are the data entry point. Generic chat is a one-off conversation. ZenovaOS AI is the layer that turns data into operating action.
| Dimension | Monitoring platform | Generic Q&A tool | ZenovaOS AI |
|---|---|---|---|
| Data source | Own collection and metering data | General knowledge, disconnected from plant data | Governed plant, device, and alarm data |
| Output form | Dashboards and alarm lists | One-off answers with unstable format | Conclusion-first reports, charts, and action lists |
| Auditability | Data is queryable; conclusions depend on people | Numbers cannot be traced | Every number traces to authorized data and evidence records |
| Scenario grounding | Fixed pages that do not follow the question | No knowledge of business scenarios or definitions | A 335-scenario library with stable output structures |
| Operating loop | Stops at the alarm; action happens offline | Ends when the chat ends | Report to action to review, as one loop |
Next step
Start from one real operating question and verify the AI output can be reviewed by the business.
Start with alarm attribution, weekly reporting, device trends, or underperforming plant analysis, then check whether the answer, chart, report, and action list are usable.