LandingLens
The computer vision system for all production companies.
Var Group and Landing AI
Landing AI is an American company founded in 2017 by the brilliant mind of Andrew Ng (currently CEO and previously head of Google Brain, then Chief Scientist at Baidu and founder of Coursera), with the mission of developing tools to apply Artificial Intelligence solutions in industry.
This business moves primarily in the field of Computer Vision, to drive the development of AI from a technology that benefits a few to a resource that benefits all. The scarcity of data in industry has so far been the biggest limitation for the introduction of such AI solutions in this sector.
Landing AI reverses the traditional approach by shifting the focus from models to data through the development of a dedicated cloud platform. The perfect combination of Var Group's expertise (particularly that of the Data Science Business Unit) to democratise the power of AI and make its adoption in businesses even easier and more effective.
Andrew Ng - Founder of Landing AI
"AI projects must create value in three ways: by reducing costs, increasing turnover or launching new lines of business."
LandingLens: intelligent Visual Inspection
Visual Inspection for the identification of product defects at the end of processing or a production phase, provides a highly effective boost to quality control.
With this computer-vision system, it is possible to:
Manage data accurately and collaboratively to derive quality data sets
Quickly train and test deep learning models on the basis of verified, high-quality data sets
Put models quickly into production on edge devices directly in the field
Areas of application
Pharma
Automotive
Mass retail
Electronics
Manufacturing
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Why choose LandingLens?
To meet the needs that manufacturing companies tell us every day: a strong need for quality control solutions that exploit Deep Learning.
Because LandingLens is a solution that brings tangible results with a small initial investment (training is possible even on very few images).
Because time frames and methods used by the factory are compatible with those of the technology: LandingLens is ready on the production line in a short time.
Because today Artificial Intelligence is democratic: with a well-defined perimeter and time frames, SMEs, too, can optimise their production processes.
Because your customers deserve the quality that your competitors cannot guarantee them: quality born of advanced technological tools.
Because Var Group has decided to focus only on the best partnerships: the perfect combination between potential and innovative expertise, and sustainability implementation.
The advantages of choosing us
- It puts Deep Learning within the reach of manufacturing businesses;
- very few images are sufficient for training;
- it adapts to different production specificities;
- it increases the scalability of adoption;
- it makes it easier to make changes;
- it standardises the workflow.
What it does
Object detection
Visual inspection with detection of specific defects in an image by assigning bounding boxes.
Classification
Assignment of a class to an entire image.
Segmentation
Visual detection of specific defects in an image: indication of the exact pixels where the defect is located.
Anomaly detection
Assignment of normal/anomaly classification for an entire image without identifying the specific defect.
LandingLens features a range of tools, including Digital Defect Book and Defect Consensus to aid developers and domain experts to manage data and correctly and collectively define relevant defects and/or anomalies.
These tools guarantee accurate labelling of data to obtain the optimal performance from the model.
From the processed and labelled data, it is possible to create the necessary data sets for training and testing models to be adopted.
These tools guarantee accurate labelling of data to obtain the optimal performance from the model.
From the processed and labelled data, it is possible to create the necessary data sets for training and testing models to be adopted.
Your first AI projects should be sufficiently significant and specific
to convince the leaders of your company to invest further in AI
to convince the leaders of your company to invest further in AI