Data health check package
Baseline data completeness, definitions, and quality across your plants and devices.
Acceptance output
A data-quality inventory with governance priorities
Pilot options
Pilot options and delivery path
Four verifiable pilot options for data health, AI reporting, alarm attribution, and underperforming plant analysis.
Pilot packages
Four pilot options with verifiable results in 4–8 weeks.
Every pilot option defines input data, deliverables, pilot participants, acceptance criteria, and review meetings up front.
AI report package
Generate weekly or monthly operating reports from real plant data and verify the quality gain.
Acceptance output
Report samples management can use directly
Alarm attribution package
Attribute and prioritize alarms so the team acts on judgments instead of noise.
Acceptance output
P0/P1/P2 priorities with attribution evidence
Underperforming plant package
Locate underperforming plants and loss sources, with a followable improvement list.
Acceptance output
Loss attribution and an action list
Delivery path
Accept one workflow first, then expand into more operating scenarios.
The path supports evaluation decisions: what each step does, what it produces, who confirms it, and when it enters daily operations are agreed in advance.
- 01
Data health check
Confirm account permissions, plants, devices, history windows, metric definitions, and quality baseline.
- 02
Scenario pilot
Run one high-value workflow on real data, including answers, charts, reports, and action recommendations.
- 03
Result verification
Compare before and after against agreed acceptance criteria and confirm whether results can enter daily operations.
- 04
Continuous review and expansion
Turn learning into scenario rules, report templates, and an expansion plan for more plants, roles, and workflows.
Phase 1
Validate the first real workflow
Pick one painful, easy-to-accept workflow, connect the existing Monitor account, and validate data boundaries, charts, and answer quality.
Phase 2
Enter daily operating cadence
Put the verified output into weekly reviews, alarm handling, device diagnosis, or mobile handoff so the operating team can use it continuously.
Phase 3
Expand to more assets and roles
Extend accepted rules, report templates, and review material to more plants, regions, roles, and operating scenarios.
Pilot participants
Pilot success depends on business, data, and acceptance alignment.
A ZenovaOS AI pilot is not only a technical connection. The pilot should clarify who provides data boundaries, who judges business value, and who confirms the result can enter daily operations.
01
Asset / business owner
Confirms pilot goals, value definitions, and final acceptance standards for business reviews.
02
O&M lead
Provides real workflows, alarm handling rules, and field feedback, then judges whether recommendations are executable.
03
IT / data owner
Confirms account permissions, data scope, history windows, and data-quality issues.
04
Acceptance owner
Reviews samples, reports, charts, and action lists at weeks 2, 4, and 8.
Before kickoff
Before the pilot starts, align scope, participants, and acceptance standards.
This decides whether the pilot can be accepted smoothly: what data to prepare, who confirms results, and which outputs enter daily operations.
Connected accounts, permission scope, and the data owner
Target plants, devices, alarms, reports, and mobile roles
Acceptance criteria for answer quality, chart evidence, reports, and field action
Transparent explanation rules for missing fields, limited history, or uncollected device data
Next step
Use one real scenario to verify whether ZenovaOS AI can enter your daily operations.
If you already know the workflow to validate, submit a pilot request. If you are still comparing scenarios, start with the role-based scenario selector.