01 / Event invisibility

Wildfire Ignition

from the distribution edge

$30B

PG&E 2017–2018 fire liability (bankruptcy)

Ignition precursors live in waveform-level behavior faster than traditional systems retain. Insurers, regulators, and plaintiffs want data utilities never kept.

02 / Frequency invisibility

IBR Oscillations

solar, wind, batteries, STATCOMs

90%+

of new U.S. capacity in 2025 was inverter-based

Oscillations at frequencies SCADA cannot resolve. A European grid collapsed in ninety seconds from interactions of this class.

03 / Behavior invisibility

Data Centers

hyperscale load as grid actor

$100B+

projected PJM ratepayer cost through 2033

On-site generation, fast-switching power electronics, and AI-driven demand curves are reshaping interconnection studies and stressing legacy models.

04 / Time invisibility

Asset Failure

aging in the dark

4 yrs

lead time for a new large power transformer

Seventy percent of large transformers in service are over twenty-five years old. They are being run harder, hotter, and longer than they were designed for.

Why It's Invisible

The grid operates across six orders of magnitude. Visibility does not.
The grid happens across hours, seconds, milliseconds, microseconds. SCADA, the instrument utilities operate by, resolves only the slowest.

Grid phenomena by native timescale, plotted against the resolution each class of instrument can capture. The shaded region is the visibility gap.

SCADA 1 Hz
PMU 30 Hz

The unlock

The sensors are already there.
The instruments that resolve every threat above have been deployed across the U.S. bulk power system for over a decade. PMUs, digital fault recorders, traveling-wave locators, power quality monitors, inverter telemetry. The visibility is not missing. The data layer is.

500,000+
PMUs deployed across the U.S. bulk power system

99.99%
of high-resolution sensor data is never analyzed

$0
additional capex required to make it visible

You have already paid for the visibility you cannot yet use.

The missing layer

The grid is already instrumented. It needs a data layer.

1
Preserve the signal. Keep high-fidelity telemetry at its native resolution instead of sampling away the evidence.

2
Contextualize the system. Connect time-series measurements to assets, topology, events, metadata, and external context.

3
Make it usable. Give engineers the query, analysis, and application layer needed to turn deployed sensors into operational visibility.

PredictiveGrid is the data layer the modern grid was missing. It turns sensors utilities already deployed into queryable, contextualized visibility. Not another dashboard. Not another silo. A foundation that makes the next generation of grid problems addressable.

10M
measurements per second ingested per node

5 PB
time-series data under management

<50ms
query latency at full resolution

You solved every problem you could see.

PingThings PredictiveGrid turns the high-resolution sensor data utilities already deployed into queryable, contextualized visibility. Not another dashboard. A foundation.