Physics-Driven
Industrial Intelligence.

The Chladni Platform combines deep learning with first-principles physics to digitize the true state of your manufacturing assets. We transform raw sensor data into a single source of truth for Yield, Energy, and Reliability.

45 Days Free • Everything Included • Cancel Anytime

The Core Engine: Multivariate Sensor Fusion

Single-variable monitoring is obsolete. To understand complex manufacturing processes, you must correlate mechanical health with thermodynamic and electrical behavior.

Single-variable monitoring is obsolete. To understand complex manufacturing processes, you must correlate mechanical health with thermodynamic and electrical behavior.

Our Physics Engine ingests three simultaneous data streams to build a holistic model of asset health:

Our Physics Engine ingests three simultaneous data streams to build a holistic model of asset health:

Mechanical Stability (Vibration)

Detects structural degradation and precision loss.

Thermodynamic State (Temperature)

Monitors friction and process thermal limits.

Process Load (Motor Current)

Quantifies torque, viscosity, and material flow.

By locking these variables together, the platform identifies anomalies that traditional SCADA systems miss—distinguishing between a machine that is broken and a machine that is simply running inefficiently.

Yield Assurance: Detecting Process Drift

In pharmaceutical and precision manufacturing, yield loss is rarely sudden—it is the result of slow, incremental drift.


Our deep learning algorithms analyze the Slope of Decay across your production line. We learn the "Golden Batch" baseline of your highest-performing runs and continuously compare real-time operations against that standard.

Capability

The system flags micro-deviations in machine harmonics and load profile weeks before they impact product quality (CQA).

Impact

You move from "Inspection" (throwing away bad batches) to "Prevention" (adjusting the process before the batch is compromised).

Energy Optimization

Energy waste is a leading indicator of mechanical inefficiency. Chladni treats energy consumption as a diagnostic tool, not just an operational cost.


By mapping current draw against mechanical output, we identify Parasitic Load—energy consumed by friction, binding, or oversized motors that produces no value.

Capability

Pinpoint specific assets consuming excess power relative to their baseline.

Impact

Immediate reduction in OpEx and automated, audit-ready data for ISO 50001 and ESG reporting.

Predictive Reliability

We replace calendar-based maintenance with condition-based precision.


Using Trend Analysis, we model the degradation curve of critical components (bearings, gearboxes, pumps). The system filters out transient noise to focus on structural wear, providing a precise timeline for maintenance interventions.

Capability

Know the Remaining Useful Life (RUL) of every asset.

Impact

Zero unplanned downtime. Maintenance becomes a scheduled, low-cost activity rather than a high-cost emergency.