deevio GmbH

We develop machine learning and deep learning algorithms for end-of-line quality control, which today is still done manually by operators. We solve the problem of consistency, accuracy and traceability of end-of-line quality control,

We have carried out several prove of concepts with paying customers and some of them are going into full production. Our customers are in Pharma and Automotive

45M jobs in Germany
24% in manufacturing: 11M
28% in Quality: 3.1M
65% in Visual Quality Control: 2M

Our SAM is 2M installations in Germany

Quality control is often still done manually. However, the task of visually inspecting parts is exhausting, prone to errors and inconsistent. Deevio’s mission is to assist the operators by automating the visual quality control tasks.

There are two million quality control operators in Germany, so our market is very big.

We develop and sell HW (AI Box + camera) and SW (customized deep learning algorithm) to assist quality control operators. Instead of looking at the object to be inspected for tens of seconds, the operator puts the object under the camera and the AI Box tells the operator if the part is a pass or a fail.

Additionally, the AI Box stores the picture of the part and links it to the ERP ordering system of the customer. We manage all the pictures, archiving and data analytics for the customer.

Cognex
MVTech
Landing.ai
Sualab

Competitors offer a platform that customers use to derive the deep learning models (similar to what Tensorflow does).

deevio offers directly the model already optimized for a specific customer inspection and all the infrastructure (computer, camera, firmware, SW) that goes with it. We offer the complete system.

Our pricing model:
NRE: 10K EU
MRR: 1K EU per month per production line

Revenue 2019: 100K EU
Revenue 2020: 450K EU
Revenue 2021: 1800K EU

Breakeven in 2021

Strengthening deevio sales force (technical sales and application engineers9 in Germany

5% of deevio

Incubated at Wattx Berlin for six months

Winner Uhlmann Pharmazing Days 2019
Winner inVISION Innovations award 2019