Edge AI Gateway

Artificial Intelligence without a cloud. No connectivity issues. No delays.

Artificial Intelligence (AI) and Machine Learning (ML) algorithms are working for industries on daily basis. Over the last few years it was possible only thanks to the strong and expensive cloud computing units. But now… AI reaches the Edge.

Smart devices do not have to be connected to the cloud anymore. Intelligent tasks can be performed on the Edge – which means directly on the device. There are no more reasons to send raw data to the cloud and wait for the answer. No more connectivity issues, no more delays.

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Artificial Intelligence (AI) and Machine Learning (ML) algorithms are working for industries on daily basis. Over the last few years it was possible only thanks to the strong and expensive cloud computing units. But now… AI reaches the Edge.

Smart devices do not have to be connected to the cloud anymore. Intelligent tasks can be performed on the Edge – which means directly on the device. There are no more reasons to send raw data to the cloud and wait for the answer. No more connectivity issues, no more delays.

check our features

Classic AI model in the cloud

Here’s why it’s not perfect:

o'clock

Latency

Each request from the IoT device to the cloud takes time. Now you see that „realtime” processing is actually not always realtime.

Costs

More devices, means more data, higher transmission and processing costs. Yes. AI in the cloud is expensive.

Security

Have you ever thought about where is your data processed? In your country? On the other continent? What about GDPR?

Connectivity

Processing a lot of data requires
a high bandwidth connection to the cloud (LTE or Wi-Fi) and it is not possible in some locations.

Edge AI starts a new age of intelligent devices

Edge AI devices can be used in virtually all applications where cloud-based solutions are used. Currently available devices with embedded AI accelerators allow putting the Edge AI in devices from single battery-powered sensors to high-resolution cameras processing Full HD images in real-time.

Cloud? Only for learning!

AI models still have to be taught in the cloud, but just after the learning period, a ready model is moved directly to the device and does not need a cloud anymore.

Real-time actions

Edge AI device has direct access to the data collected from IoT sensors. It can understand the data and take immediate actions like notifying the user, lighting a red light or turning the voice signal on.

Offline mode

Edge AI device will work independently on the connectivity. It can be designed to send reports or summaries whenever the network is available i.e. once a day or once a month. Or it can work offline constantly if that’s assumed business scenario.

Edge AI Gateway - our solution

Imagine you can connect all your sensors to the industrial Edge AI Gateway.
Such a gateway is able to understand data from multiple sources and take actions based on the real-time data. It doesn’t matter if those will be vibrations for predictive maintenance purposes or video from the embedded camera for recognition of components on the production line.

What we offer is a Gateway ready to be enhanced for your business scenario.
We can equip it with cameras, input/output ports, connectivity modules and other components
based on your needs.

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What does it mean for you?

universal base-device

that you can combine with every sensor
or system according to your business needs. You have endless number of possibilities.

reduced costs

even with many devices, sim cards or routers your costs are always smaller in comparison to cloud sollution with similar data transmission stream

data security

data processing is always secure and you don’t have to worry about law regulation in different countries or GDPR.

connectivity independence

Edge AI does not require high bandwitch connection with the cloud. It is working in every location. Sending data can occur only in some defined time periods. .

Have questions?

Let’s talk!

Mateusz Majchrzycki
IoT Team Leader

Paweł Skiba
Head of RapidLab

Mateusz Majchrzycki
IoT Team Leader

Paweł Skiba
Head of RapidLab

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