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Solar Intelligence Platform
Physics-calibrated performance intelligence for solar fleets — every megawatt-hour of loss attributed to a cause.
Manufacturing
Architecture readyDesign intent as the baseline, not last month's average.
Process lines and rotating equipment, modeled before they fail.
The operating problem
- 01
Condition monitoring is bolted onto critical machines and absent everywhere else, so failures happen where nobody was looking.
- 02
Process drift is discovered at quality inspection, after the material has already been consumed.
- 03
Maintenance windows are allocated by calendar, so healthy machines are opened and degrading ones are not.
How Peryx applies
- 01
Equipment is modeled against design intent and commissioning behaviour rather than against a rolling recent average.
- 02
Process and equipment signals are read together, so a drift in output is traceable to the machine causing it.
- 03
Interventions are sequenced by predicted consequence within the maintenance window available.
Under the hood
The pipeline does not change between sectors.
Only the physics model in the middle is sector-specific. That is the whole argument for expanding beyond solar.
Sensors
Telemetry
Physics model
Digital twin
Insights
Recommendations
Decisions
Where we actually are
Architecture readyModeling approach designed and ready for a scoped pilot.
Architecture ready. The modeling approach is designed for this asset class and a scoped pilot is the next step.
Solar is the sector where this architecture has been proven against real operational data. If you operate manufacturing assets and want to know whether it transfers to yours, the honest answer comes from looking at your data together — not from a page like this one.
See the platform
Bring your hardest question.
Mixed vendors, sparse instrumentation, an anomaly nobody can explain. The first call is with someone who can discuss the detail.