Quality Control

AI for detection and automation

In an increasingly competitive production environment, quality is a key differentiating factor. Lack of quality represents a high risk to performance and competitiveness, which leads to very high costs for industries. Machine Learning and Deep Learning algorithms contribute today to the increasing automation of quality control in production lines, helping to significantly reduce the number of defective parts and the consequences of this.

Icono Quality Control


Full automation remains a major challenge for companies, as quality control still relies heavily on the visual ability of humans and their limited ability to adapt to potentially changing conditions. On the other hand, Artificial Intelligence, provided it is fed with adequate and good quality data, is not restricted, by its nature, by physiological or variability limitations.

Factories that create complex products, such as microchips and circuit boards, use Artificial Vision models, in which AI is complemented by high-resolution cameras, so that details and almost imperceptible errors can be captured with much more reliability than the human eye. When integrated with a cloud-based data processing framework, errors are flagged instantly, and the response is automatically coordinated.



  • Prediction, detection and classification of defects by measurement and management of both images and text, as well as other process data
  • Optimal machine configuration prediction in case of changes to achieve zero defects
  • Estimated useful life


Among the most used solutions in this area we can find those based on the analysis of automatically generated documents with information about the evolution of the processes, status of the products and the recognition of defects through images of the products. For example, the analysis of the status of software executions (logs) and the behavior of the servers on which they operate for the detection of code or hardware errors are some of the most common applications in quality control. Our customers have already implemented automatic classification tools for incidents in the production of parts as a mechanism to increase the speed of response to them.

Quality control can also take place at the end of the line, to check compliance with the rules of presentation of a product. It is an increasingly widespread practice to incorporate video control systems powered by Artificial Intelligence that check that the product characteristics really match the company’s quality standards.


With the Agile methodology as an internal standard, we always adapt to the requirements and availability of the Client.

Since the services offered are customized, we consider frequent and close communication and contact as essential conditions to maximize the efficiency and continuity of the projects. That is why the feedback and progress presentation meetings have an average frequency of one week.


How can I incorporate AI into my processes?

Answer All of our collaborations are based on support from the conceptualization phase to the production of the solutions. The first step is to contact us and, without any commitment, find the optimal way to apply the tools that best suit your business.

Do I need a Data Scientists team and a special data architecture?

Answer Not necessary, since we have a complete team of experts and the necessary computer resources for the development of practically any type of solution based on data and Machine Learning. Flexibility in the nature of the data source is complete (unstructured data, SQL databases, Excel data, …)

How can I measure the value you bring to my business?

Answer The calculation of profitability can be obtained in a quantitative way as in traditional projects. In addition, the support of success stories and the strong investment made by companies all around the world is a true reflection of the reality about these projects and how the risk associated with them is, year by year, decreasing.

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