We are an innovative company of Artificial Intelligence

We form a multidisciplinary team of specialists in technological innovation, united to develop solutions according to the new times.

We enjoy creating innovative products, adapted to the new needs of the market, we want to be benchmarks in Artificial intelligence and cybersecurity, sharing and applying our knowledge at the service of our customers.

Our passion

innovating is the main axis of our vision. We are committed to offering innovative technologies capable of solving technological and logistical vulnerabilities.

Our valors

Our vision and strategy are defined by our passion for technology and innovation. Our values characterise all the work we do at CIBINAR, creating a culture that helps us create high quality products.

Commitment.To achieve maximum performance in everything we do, and to preserve our working culture with which we support our employees in their growth.
Constancy.To build every day a path of excellence that impacts on quality products and responds to global technological advances.
Integrity.Transparency and respect are aspects that define us and constitute fundamental pillars in our day-to-day work team.
Honesty. With which to maintain our status. For us, honesty with our clients is paramount, always prioritising their needs.

Our tools

We build on our own developments based on state-of-the-art technologies:

Artificial Intelligence

Artificial Intelligence refers to systems that mimic aspects of human intelligence to perform tasks that humans could perform only in the past.

These systems can improve iteratively from the information they collect. An Artificial Intelligence system is able to analyse data in large quantities and identify patterns and then function automatically with speed and determination.

Machine Learning

Machine learning is the scientific study of statistical algorithms and models that computer systems use to perform a specific task without using explicit instructions, but are based on patterns and inference.

Machine learning algorithms are used where it is difficult or unworkable to develop a conventional algorithm to perform the task effectively, such as email filtering and artificial vision.

Machine learning is closely related to computational statistics, which focus on making predictions using computers. The study of mathematical optimisation offers methods, theory and application domains in the field of machine learning.

Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application through business problems, machine learning is also known as predictive analysis.

Deep Learning

Deep Learning is a subset of Machine Learning. It is a multilayer neural network that learns from large amounts of data. This network is similar to the neural connections that exist in the brain.

It stands out because it does not require previously scheduled rules, but the system itself is able to learn for itself. Deep learning is mostly used for the automation of predictive data analysis.

Artificial Vision

Artificial vision or computer vision is a scientific discipline that includes methods to acquire, process, analyse and understand images of the real world in order to produce numerical or symbolic information so that they can be treated by a computer.

Just as humans use our eyes and brains to understand the world around us, artificial vision tries to produce the same effect so that computers can perceive and understand an image or sequence of images and act as appropriate in a given situation.

This understanding is achieved through different fields such as geometry, statistics, physics and other disciplines. Data acquisition is achieved by various means such as image sequences, views from multiple video cameras or multidimensional data from a medical scanner.

Artificial vision is in charge of obtaining images in order to process them, adapt them (image dimension, image color, etc.), perform an analysis of the various qualities of a person’s face, and extract this information for a specific classification.