IA

Process Model

We define the project goal and analyze the business needs, gathering relevant information. Based on these requirements, we use various techniques and technologies to optimize, automate, and implement customized solutions.

Information Extraction

We carry out the collection, preparation, and exploration of data needed for analysis. This includes tasks like cleaning, transformation, and descriptive visualization, which allow us to assess which models will be most suitable.

Modeling and Evaluation

We build and evaluate predictive models using machine learning techniques. We propose optimized models that provide explanations for decisions based on source data. Our experience in model creation, combined with rigorous data analysis, enables the generation of effective solutions quickly, facilitating their implementation.

Techniques

Machine Learning: We identify models or patterns in data-rich environments that allow predictions or decisions to be made through supervised, unsupervised, and reinforcement learning.

Deep Learning: We use convolutional and recurrent neural networks to detect complex patterns in data, also exploring techniques like generative adversarial networks.

Scenarios

Video Analytics: We identify objects and people in images with very high accuracy.

Language: Ability to understand and generate human text.

Information Integration and Normalization: We incorporate different types of data aligned with the domain of interest, performing specific actions to clean datasets and optimize models.

Network and Infrastructure Assurance: We implement processes and technologies to ensure the availability, performance, and security of networks and digital systems through proactive monitoring and management of assets to prevent issues before they affect the end user.

Use Cases

Close-loop Automation: Continuously monitors and adjusts a system based on its actual output, comparing it to a target, keeping variables within a specific range.

Root Cause Analysis: A systematic methodology for identifying the underlying cause of problems, focusing on lasting solutions to prevent recurrence.

Recommendation: Uses data to predict products, services, or content of interest to the user community, facilitating discovery and improving customer satisfaction.

Anomaly Detection: Machine learning techniques that identify unusual patterns or outliers in data, highlighting exceptional events, errors, fraud, or unexpected behaviors.

Predictive Analytics: Analyzes historical and current data to predict future events and estimate possible scenarios