We apply Artificial Intelligence to our projects to minimize errors, reduce time, facilitate decision making and increase the quality of our clients' operations, whether they are private companies or public organizations.
We use Machine Learning Techniques for the estimation of runoff coefficients and their variation over time or the magnitude of damage to infrastructure. The Pattern Analysis is another of the elements of Artificial Intelligence that are part of our day-to-day projects, as they are very useful for extracting information and creating clusters or conglomerates, as happens when using socioeconomic data from surveys, We create profiles of people with common characteristics so that our employees in the Humanitarian Sector can detect communities that require assistance as a priority compared to other profiles.
On the other hand, the Self-Organising Maps (SOM) are a type of unsupervised learning algorithm that we use to visualize and classify complex data in a two-dimensional representation. They work by creating a map structure where each node represents a group of similar patterns in the input data, which makes it easier for us to analyze each generated profile.
Finally, we use tools for Web Scraping and Social Media Scraping to obtain information on topics of interest to detect problems that affect or are of interest to society, such as the price of the Basic Food Basket. This process of information extraction is also useful to create time series and to make predictions with the use of the information from the data, such as the number of people living in the country, insecurity, food and medicine shortages or natural catastrophes, and to perform language processing, which allows us to identify and analyze the sentiment of the population with respect to a specific topic. This process of information extraction is also useful for creating time series and making predictions with ARIMA and techniques of Deep Learning (LSTM) to help institutions make provisions in their budgets.