Intelligent technologyfor the digital world.
We utilize the limitless potential of AI to create technology for the next century. Our goal is to meet the needs of digital businesses through the application of pioneering software intelligence.
Big Data Ecosystem
We combine the power of Big Data with the analysis, optimization and visualization capabilities offered by current tools.
Cognitive Analysis
Multi-platform virtual agents that can interpret and respond to human language and engage in conversation.
Machine Learning
Systems that identify complex patterns using immeasurable volumes of data, and are able to reliably predict scenarios.
01. Big Data Ecosystem
Big Data Architecture
BIg Data architecture consisting of data-centered layers: data transformation, storage and processing, exploration and management, and serving through APIs.
Data Science
Science that applies methodologies, processes and systems to extract knowledge and understanding of structured or unstructured data from multiple sources.
Predictive Models
Advanced analysis that uses new and historical data to predict future activity, behavior and trends. Applies the Predictive Model Markup Language (PMML) and uses application programming interfaces.
Data Analytics
Includes data collection, integration and visualization services, enterprise analytics solutions, social media and digital outreach, policy analytics and decision support, and data quality assessment.
02. Cognitive Analysis
Artificial Vision
Uses a camera to capture and analyze visual information, ranging from signature identification to medical image analysis, with a level of efficiency and precision far superior to that of the human eye.
Natural Language Processing
Understanding the real meaning of any query executed (semantics). Through computational linguistics, computers can decipher human language and imitate conversation.
Cognitive Computing
Uses many of the same fundamental aspects of AI—such as ML, ANN, NLP and sentiment analysis—to replicate human problem-solving processes. Systems that scale their learning, use reasoning and interact with humans.
Artificial Neural Networks
Based on a collection of connected units or nodes called artificial neurons, an ANN is a computing system that learns progressively by considering examples, generally without task-specific programming.
03. Machine Learning
Unsupervised Learning
Dimensionality reduction (meaningful compression, structure discovery and Big Data visualization) and clustering (customer segmentation, targeted marketing and recommender systems).
Reinforcement Learning
Focused on real-time decisions, robot navigation, artificial intelligence in gamification, skill acquisition and learning tasks. Uses a trial and error method to identify the most rewarding actions.
Supervised Learning
Based on building patterns, including classification (customer retention, fraud detection, diagnostics) and regression (marketing and sales forecasting, estimating and growth predictions).
