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AI-Box - Smart Visual Inspection

deevio automates visual inspections in production. Our core product is an artificial intelligence-based machine vision software with which we help manufacturing companies to automate their still manually performed quality inspections and to significantly improve their existing machine vision systems. 

We supply our customers with a camera system tailored to the individual application, which can then be integrated into the respective machine environment via a PLC connection.

The experience we have gained over the past years by implementing inspection cases, has helped us to develop hardware and software to meet all the requirements of an AI-driven inspection workflow.

GigE Vision and GeniCam

Supporting cameras through industry standards means you can choose from a wide range of options of grayscale, color, or even 3D cameras to suit your use case.

Power over Ethernet (PoE)

With PoE, cameras can directly connect data and power with a single cable, supporting up to 100m in length.

Real-time GPIO’s

A dedicated FPGA enables real-time control of GPIOs for optimal setup of triggers for light and camera as well as other external peripherals. 

1. Acquire & Label Images

Any visual inspection starts with taking images through cameras. With the AI-Box, cameras and lights can be directly connected without the need for intermediate switches or controllers. Using High-Dynamic-Range (HDR) photography, even highly reflective surfaces can be acquired with sufficient local contrast.

The AI-Box also has labelling software that allows you to draw a detailled label on the actual defect in the image. This way, you teach our deep learning software what makes a defect and what not.

deevio: acquire and label training images

2. Inspect Images

The core of the AI-Box runs a deep learning algorithm that has now learned how to distinguish images from “OK” and “not OK” products. Based on the images and labels from the first step, deevio trains a deep learning model that is tailored to your use case. 

Unlike traditional rule-based inspection, our AI is trained on those labeled images that you provided.


This enables a new workflow where the bottleneck is no longer the vision engineer coding the inspection, but where the AI itself learns how to inspect.

Binary and multi-class classification

for each input image, the AI algorithm returns a single OK/NOK answer. Or you extend the training data by including different error classes, and our algorithm learns to distinguish multiple types of defects


for each input image, the AI algorithm can show exactly where the defect is in the image.


in cases where the defect covers a large part of the image, our algorithm recognizes when an image contains an anomaly not seen before. This algorithm can be trained utilizing only images of good parts and does not require the customer to provide images of defects.

3. Manage Your Inspection Case

AI-based solutions require a dedicated workflow, this is supported by the AI-Box: 

deevio: manage complex inspection workflows


Create a new product, add classes or defect types you want to identify, and start acquiring and annotating images. 


Review images, remove wrong labels or adapt annotations.


Let deevio create a deep neural network-based model that is tailored to your new product and upload it to AI-Box


Change your inspection to the new model and start inspecting on the production line!​


Create, review, adapt and export data sets that are ready to train an AI model.


​Annotate images with use case-specific labels. Draw on top of images with our labeling tool to mark the exact location of a defect.


Data export: No lock-in, you can easily export images and annotation data from AI-Box


The AI-Box application is optimized to run on the AI-Box, a small and efficient machine vision PC that is built to our design.

In addition, the software stack runs on a wide range of NVIDIA-GPU-enabled PCs to serve any requirements for cycle time, image resolution, and complexity of the AI-models. 

deevio: AI-Box with high connectivity

Low energy 




2xPoE/1x Gigabit Ethernet


Digital IO


24V DC


System on a Chip (SoC)

Nvidia Tegra X2, 64 Bit


4-core ARM Cortex-A57, 2GHz

2-core Denver2, 2GHz


256 CUDA cores (Pascal), 1.12 GHz




1 x M.2 SSD (256 GB)

1 x 32 GB eMMC

1 x SDXC slot


Camera Interface

2 x GigE Vision with Trigger-and-Power-over-Ethernet

Camera Trigger

2 x Trigger-Over-Ethernet

Digital I/O

8 x Input & 8 x Output, opto-isolated, 24 V

Encoder Interface RS-422

3 x input for one encoder


1 x 1000 MBit/s

Wireless LAN

1 x IEEE 802.11a/b/g/n/ac with external antenna socket

LTE (4G)

1 x internal socket for SIM, with external antenna socket


2 x USB 3.0 ports, 1 x micro USB maintenance port 


1 x DisplayPort

Power & Dimensions

Power Supply

24 V (DC) 

Thermal Solution

fan-less, via heat sink

Dimensions (W x D x H)

163 mm x 163 mm x 48 mm (without mounting plate)

163 mm x 210 mm x 48 mm (with mounting plate)

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