Data acquisition and processing
Signals from sensors and devices: continuous collection, cleaning, normalisation and queryable storage.
- Sensor integration
- Signal cleaning and normalisation
- Time-series storage
The registered framework under every implementation we build with AI, sensors and robots. It is also what Podz.AI is built on.
Or write to us: info@ggtechnologies.sm
Everything we deliver — a medical wearable, a robotic cell, an AI that reads documents — sits on the same layer of technology. That layer has a name: DigiSense®, our framework, a registered trademark of G&G Technologies S.r.l.
We did not build it to sell it. We built it because the same problems kept coming back in different sectors: take a reading from a sensor, clean it, let a model read it, make a machine act on it. Solved once and properly, they can be reused anywhere.
DigiSense® is a registered trademark of G&G Technologies S.r.l.
The technology base shared by wearables, robotics and artificial intelligence.
Designed and developed entirely in Europe.
Signals from sensors and devices: continuous collection, cleaning, normalisation and queryable storage.
Models run on your own machine or server. When the cloud is needed, personal data is masked first.
The same layer that reads the data drives the automation: control logic, safety and dialogue with the line.
Vital-signs acquisition, and the platform that makes the data readable to a doctor, coach or carer.
Machine control and production data collection on the same technology base.
Task-specific agents built on the DigiSense® data layer.
Our product: the personal AI workstation, built entirely on DigiSense®.
No. DigiSense® is the framework we build with, not an off-the-shelf package. The product built on DigiSense® is Podz.AI, and that is downloaded from the product site.
You do not start from scratch. Data acquisition, running the models and machine control are already built and proven. The work focuses on your problem, not on the plumbing underneath.
Because the three layers — sensors, data, models — have to talk to each other in our sectors. Keeping them in-house lets us work on the whole chain, instead of stopping at the boundary of somebody else's product.
Yes, we are interested. If you have a problem that touches sensors, data and automation, write to us: the first conversation is about finding out whether there is common ground.