Monitor data
Reuse existing accounts, permissions, plants, devices, alarms, generation, and reports.
Public architecture
Turn monitoring data into AI operations capability teams can act on every day.
ZenovaOS AI connects to your existing monitoring platform and turns plant, device, alarm, generation, and knowledge-base data into an explainable, reusable, and verifiable AI operations layer. Teams can identify issues faster, organize evidence, generate charts and reports, and continuously turn field experience into product capability.
From data to operating loopExplainable · Verifiable
01
Connect existing monitoring assets
02
Create the operating semantic layer
03
Deliver standard operating scenarios
04
Evolve through co-creation delivery
Architecture overview
One diagram explains how ZenovaOS AI turns monitoring data into operating capability.
This chain drops straight into a briefing deck: the left side is your existing monitoring data, the right side is the reports, charts, and action loop teams use every day, and the middle keeps evidence, scenarios, and delivery trustworthy.
01 02 Evidence units
Organize KPIs, trends, alarms, device status, and KB content into traceable evidence.
03 Scenario flows
Structure evidence around alarm governance, generation review, device diagnosis, and deep reports.
04 Deliverables
Output conclusion-first answers, charts, reports, action lists, and mobile handoff material.
05 Co-creation evolution
Turn field feedback into rules, templates, acceptance samples, and expansion plans.
How it works
A product path from monitoring data to an operating loop.
The result is a continuous workflow: use real data to find issues, organize evidence, form conclusions, generate reports, and turn review experience into reusable capability.
01
Connect existing monitoring assets
Keep the existing monitoring platform in place and reuse the data and permissions you already have.
Plants, devices, alarms, generation, PR, weather, KB, and conversation history become one evidence source.
02
Create the operating semantic layer
Translate data fields into business objects such as plants, devices, alarms, metrics, and actions.
AI does more than read data. It organizes analysis around asset relationships and operating language your team already uses.
03
Deliver standard operating scenarios
Turn high-frequency questions into repeatable AI workflows.
Alarm governance, generation review, device diagnosis, and reporting all follow fixed evidence structures and acceptance standards.
04
Evolve through co-creation delivery
Field feedback becomes product capability instead of staying inside a single project.
Pilots, reviews, and acceptance cycles become scenario rules, report templates, acceptance material, and pre-launch review mechanisms.
Capability model
Three layers make AI output trustworthy, stable, and deliverable.
Every answer is supported by evidence units, organized by scenario flow, and rendered into material that can be used in meetings, reports, and daily handoffs.
Atomic
Evidence unit
Standardize operating evidence such as KPIs, trends, alarms, device status, and recommendations so every conclusion has a source.
KPI card
Alarm summary
PR trend
Action recommendation
Composition
Scenario flow
Organize evidence in the order real decisions happen: assess impact, locate cause, recommend action, and support review.
Alarm governance
Generation review
Device diagnosis
Deep report
Render
Deliverable result
Render analysis into charts, reports, mobile content, and executive summaries teams can use directly.
Chart package
Report
Executive summary
Mobile handoff
What you see
These AI operating scenarios are ready to use.
Each scenario follows a real workflow: answer the business question, present evidence and charts, then recommend the next action.
Alarm governance
Too many alarms, unclear priority.
Group repeated alarms and provide priority, impact scope, likely cause, and action list.
Move the O&M team from alarm lists to action sequencing.
Generation and PR review
Weak output is hard to attribute to weather, equipment, or operations.
Show trends, PR movement, anomaly dates, cause signals, and review conclusions.
Give asset owners and O&M teams the same evidence base for yield and responsibility discussions.
Device diagnosis
Inverter, string, and meter signals are scattered across pages.
Combine device status, power curves, anomaly ranking, and troubleshooting suggestions.
Turn field-engineer know-how into reusable diagnosis paths.
Deep reports
Monthly reports and management material require heavy manual work.
Conclusion-first report, chart evidence, action plan, and reviewable HTML report.
Turn reports from one-off material into a renewable operating asset.
Co-creation delivery
Co-creation delivery (FDE) turns field needs into scalable product capability.
Co-creation delivery is ZenovaOS AI's methodology: identify high-value scenarios, build verifiable samples with real data, then turn feedback into reusable capability.
01
Prioritize high-value workflows
Select the first scenarios from operating meetings, O&M teams, and asset reviews.
02
Validate data and boundaries
Confirm data interfaces, fields, permissions, history, and unavailable-data reasons so results can be verified.
03
Deliver real-data samples
Use real data to generate the first answer, chart, report, and action recommendation.
04
Productize the feedback
Turn feedback into scenario rules, KB, acceptance samples, report templates, and a continuous-review cadence.
Why it is credible
The final delivery is a sustainable AI operating capability.
These four points close the architecture story and drop straight into an executive briefing.
Preserves existing system investment
ZenovaOS AI works on top of your existing monitoring data and permissions without requiring platform replacement.
Results are traceable
Charts, reports, and actions trace back to authorized data, business definitions, and evidence.
Capability is repeatable
Evidence units and scenario playbooks expand across plants, regions, and customers.
Pilots are verifiable
Co-creation delivery turns feedback into sanitized replays, pre-launch review, acceptance records, and review material.