We read the signal from your equipment and catch the failure before the downtime.

Factories, farms, and fleets generate constant sensor data: vibration, temperature, humidity. Today, detecting when something is about to fail requires routing that data through several different systems. We analyze the signal and cross-reference it with business context (which machine, which shift) in a single step.

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Is this for you?

Manufacturing: predictive maintenancePrecision agricultureTransport fleetsStructural monitoring of buildings and bridgesMedical equipment

Against the typical stack, here's what you gain:

Usual stackWith Gorigami
Storing the sensor time seriesInfluxDB or TimescaleDB, separate from the rest of your dataA Vector column alongside relational metadata (machine, shift, line)
Processing the signal (FFT, filters)Exporting to a separate Python/numpy processNative FFT, PSD, and BANDPASS in the engine
Cross-referencing signal with business contextManual join between two separate systemsA single query over one table
Where the analysis runsA central cloud serverCan run at the edge, on a gateway inside your plant

The architecture, quickly:

How we implement it

We ingest the sensor signal as a Vector per time window, apply PSD/BANDPASS/WHITEN to isolate the signal of interest, and MATCHED_FILTER to detect a known failure pattern, all in the same table as machine_id and shift. It deploys as a single binary on a local gateway: only the alert goes up to the cloud, never the raw stream.

  • Diagnosis of critical assets and available signals
  • Anomaly detection models by frequency (vibration, current, temperature)
  • Alerts wired into your maintenance flow (email, WhatsApp, CMMS)
Predictive maintenance with sensors

Frequently asked questions

Do I need new sensors, or can we use what I already have?

In most cases we work with the sensors and drives your plant already has. We start with a diagnostic of critical assets and available signals before recommending any new hardware.

Does my data stay in my plant, or does it go to the cloud?

It depends on your preference: the analytical engine can run embedded inside your operation (a local server or gateway) or in the cloud. Either way, the data is yours.

How many sensors can the system handle?

The engine is built for plants with tens to hundreds of sensors per site. For larger scale, we integrate it with distributed processing tools.

How do alerts reach my maintenance team?

We wire alerts into your existing workflow: email, WhatsApp, or your current CMMS, without forcing you to switch management tools.

An honest boundary before moving on:

What we don't solve

We don't replace large-scale real-time ingestion (Kafka, an MQTT broker) or a visual dashboard: you'll still need Grafana or something similar, and a message broker if the volume is massive and distributed. The sweet spot is tens to hundreds of sensors per site, not millions distributed globally in real time.

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