Solar operations
Follow DC power, AC power and conversion efficiency from solar inverters alongside site weather conditions such as irradiance and ambient temperature, in one live view.
Use cases
Each use case is grounded in what the platform does today. None depends on a roadmap capability.
Follow DC power, AC power and conversion efficiency from solar inverters alongside site weather conditions such as irradiance and ambient temperature, in one live view.
Track battery state of charge, temperature and voltage for your storage assets, live and over time.
See your sites side by side, organised the same way, instead of spreadsheet by spreadsheet.
One place for teams that currently juggle several disconnected tools, with a view tailored to each role.
Weather and site sensor data usable across every relevant asset, without duplicating sensors per device.
Early preview
Use preview AI-generated insights as a starting point for operational review, alongside your team's own judgement.
Battery storage insight
Published incident research on battery energy storage finds that most incidents do not start with a failed cell. They start with controls, integration and day-to-day operating discipline.
NeoAI models the battery together with its controllers, devices and sensors, so the whole system is visible, not just the cell.
Source: EPRI BESS Failure Incident Database.
Not available today. A designed direction, built on the same telemetry foundation.
Remaining-useful-life estimates from calendar age, cycling, temperature exposure and capacity trend.
Warranty conditions turned into measurable operating indicators, once your manufacturer's terms are configured.
The cost of battery ageing per MWh moved, as a figure you can use in dispatch decisions.
The value of a charge or discharge, net of losses and the cost of ageing the battery.
Tell us which of these matters most when you request a demo, and we will focus the conversation there.
Health status shown for any asset is rule- and threshold-based. NeoAI does not currently offer predictive or machine-learning-based asset health.