Artificial Intelligence

The creation of systems that learn, adapt and potentially act autonomously has become a challenge for technological developments for the coming years.

What is AI (Artificial Intelligence)?

AI can be defined as technology that tries to explain mental functioning based on the development of algorithms to learn, think and take decisions. AI combines several fields, such as robotics, expert systems and others, which have the same objective: to create systems that can think for themselves.


The use of AI aims to improve decision making and reinvent business models and ecosystems. Such tasks reduce costs and risks in human handling in hazardous areas and improve performance and quality control.


Most AI investment follows one of three patterns:

Automated Process Optimization

Technologies: Predictive Analytics, Prescriptive Analytics, Reinforcement learning, Machine Learning, Deep Learning

Cognitive Expertise

Technologies: Sentiment Analysis, NLP, Unestructured Data

New Interfaces

Technology: Bots

Detect Hidden Patterns

Spot Deviating behavior

Anomalies Detection

Correlation between process variables


Workflow Optimization

Consumer behavior in real-time


Increase Security

Facial Signature in Forms

Anomalies Detection in access methods

Help Users

Case Auto Classification

Problems detection with potential customers

Outcome Prediction along process life

Actionable Insights

Informs company strength

Bots for internal/external user

Predict Future

Process Simulation for Bottlenecks

Business Metrics Predictions

Predict Future consumption trends

Customer Churn

Actionable Insights

A few examples where Artificial Intelligence can be useful:

A few examples where Artificial Intelligence can be useful for any business / Algunos ejemplos donde la Inteligencia Artificial puede ser útil para cualquier empresa

Example #1
Outcome Prediction

Prediction models can be used to obtain a prediction of a specific result of a process, and update this prediction in light of new events, or the passage of time. It can be used to detect when an opportunity is at risk, when a process is likely to be relevant and relevant.

Example #2
Detection of Anomalies

The automatic detection of outliers and anomalous patterns is one of the most common scenarios of application of the Machine Learning / Deep Learning models. Although its implementation by means of multi-variational time series is well known, the last years are being surpassed by models based on Recurrent Neural Networks (RNN).

Example #3
Recommendation Engine

Although AuraPortal already implements its recommendation mechanism for actions based on the outcome of the latest processes, the AI ​​can provide the System with more advanced model combinations, anomaly detection and automated data pre-treatment.

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