Services

Artificial intelligence consulting

Algorithms that detect patterns and learn from experience

There are certain problems or decisions in which the human mind is unbeatable. However, there are also many processes and analyses in which people are not highly efficient. Usually this occurs in problems with a high mechanical component or when the best solution relies in finding patterns in large volumes of data.

In companies, analytical processes are often solved in an intensive but imprecise way.

Either manually by a qualified person. Often inaccurately and spending a great deal of time.
Or by building an automatic process from a set of fixed rules.

At Prenomics we help organizations identify this type of processes and we build Machine Learning algorithms that help solve them in an automated way. Constantly re-learning and establishing mechanisms to review the integrity of the algorithm performance over time.

Data Modeling

To understand patterns in data and be able to predict a target behaviour.

Algorithms Construction

End to end solutions to business problems based on AI.

Deployment in productive environments

Use, monitoring, and calibration of algorithms on a daily basis.

Data modeling using Machine Learning

We answer business questions using machine learning techniques. To do so:

We understand the business objective.
We compile the working dataset.
We define the most appropriate modeling strategy.

Construction of algorithms based on Machine Learning

We develop end to end algorithms that incorporate artificial intelligence to maximize forecasting power and allow for better decision making.

Thanks to this approach, the algorithms are robust against situations that have not been previously defined or experienced, learning from their performance and calibrating themselves autonomously based on it.

The range of applications for this type of algorithms is very extensive. They can be used for cases as varied as a product recommender in an e-commerce, a prioritizer of which orders must be prepared and served first in a factory or an adaptive learning algorithm that guides a student in a school curriculum.

Deployment in productive environments of self-learning algorithms

Some machine learning models only make sense to run occasionally, however other models and algorithms can have an impact on the day-to-day operation of the organization, and it is convenient to re-run and re-train them daily or even more frequently.

In these cases we prepare the model for its industrialization. Focusing on:

Optimize its efficiency.
Facilitate ways to incorporate human feedback and retrain the model with the normal day to day operation of the company.
Providing performance metrics that allow to evaluate the quality of the model fit, predictive capacity and alerts in case its performance degrades.