# PingThings

PingThings is the time-series data foundation for physical systems. Our platform, PredictiveGrid, ingests every signal a utility's grid produces — sensor data, operational systems, external context, analytical outputs — and turns it into a single time-aware substrate that engineering teams build on. Customers include major transmission and distribution investor-owned utilities, ISOs, and energy research institutes across North and South America.

Our argument is not "we have a time-series database." It's that physical-system operators need a use-case factory: a platform their team can use to solve the problems they have today with the data they already have, and to solve problems years ahead of when the rest of the industry has commercial tools for them. Use cases compound. Every solved problem leaves behind reusable queries, models, workflows, and operating knowledge, so the next one ships faster than the last.

## Key links

- [Use Cases](https://pingthings.io/use-cases/) — what teams have built on PredictiveGrid, organized by the operational question being answered. Includes a representative library and four customer case studies covering oscillation root-cause analysis, large-scale AMI-driven topology and phase identification, automated lightning event detection across a national PMU fleet, and ongoing forensic post-mortem analysis on transmission events.
- [Platform](https://pingthings.io/platform/) — the storage, query, and analytics architecture beneath the workflows.
- [Capabilities](https://pingthings.io/capabilities/) — how the platform handles any sensor, any frequency, full-fidelity historical depth, and AI/application workloads on the same query layer.
- [Contact](https://pingthings.io/contact/) — talk to an engineer.

## Background

PingThings was founded in 2017 and is headquartered in Anaheim, California. The product is purpose-built for the energy transition: handling the data volume and modality explosion as utilities ingest synchrophasors, AMI 2.0 telemetry, distributed energy resource endpoints, distributed fiber sensing, and other emerging time-series sources alongside legacy SCADA and historian data. Engineering teams at major North American and South American utilities use PredictiveGrid to build use cases that would otherwise require multi-year vendor engagements.

The platform's distinctive claim is the "use case factory" — the compounding effect of solving each new problem with the team's own data, where each use case becomes a reusable building block for the next. This is structurally different from buying a single vendor application or assembling a general-purpose data warehouse, both of which create new lag every time a new problem appears.