Brighterion combines 10 cutting-edge Artificial Intelligence and Machine Learning technologies, including our unique, patented Smart Agents

The Power of 10

Brighterion offers the world’s deepest and broadest portfolio of artificial intelligence and machine learning technologies

Smart-Agents is the only technology that has the ability to overcome the limits of the legacy machine learning technologies allowing personalization, adaptability and self-learning. Smart agents do not rely on pre-programmed rules and do not try to anticipate every possible scenario. Instead, smart agents create profiles specific to each entity and behave according to their goals, observations, and the knowledge that they continuously acquire through their interactions with other smart agents. Each Smart agent pulls all relevant data across multiple channels, irrespectively to the type or format and source of the data, to produce robust virtual profiles. Each profile is automatically updated in real-time and the resulting intelligence is shared across the smart agents. This 1-to-1 behavioral profiling provides unprecedented, omni-channel visibility into the behavior of an entity. To learn more, please click on the link below.

Next Generation, Artificial Intelligence and Machine Learning

Data mining, or knowledge discovery in databases, is the nontrivial extraction of implicit, previously unknown and potentially useful information from data. Statistical methods are used that enable trends and other relationships to be identified in large databases.
The major reason that data mining has attracted attention is due to the wide availability of vast amounts of data, and the need for turning such data into useful information and knowledge. The knowledge gained can be used for applications ranging from risk monitoring, business management, production control, market analysis, engineering, and science exploration. To learn more, please click on the link below.



A neural network (NN) is a computer program inspired by the structure of the brain. A neural network consists of many simple elements called artificial neurons, each producing a sequence of activations. The elements used in a neural network are far simpler than biological neurons. The number of elements and their interconnections are orders of magnitude fewer than the number of neurons and synapses in the human brain.
Backpropagation (BP) [Rumelhart, 1986] is the most popular supervised neural network learning algorithm. Backpropagation is organized into layers and connections between the layers. The goal of backpropagation is to compute the gradient (a vector of partial derivatives) of an objective function with respect to the neural network parameters. Input neurons activate through sensors perceiving the environment and other neurons activate through weighted connections from previously active neurons. Each element receives numeric inputs and transforms this input data by calculating a weighted sum over the inputs. A non-linear function is then applied to this transformation to calculate an intermediate state. To learn more, please click on the link below.

Deep Learning/ Neural Networks

Case-based reasoning (CBR) is a problem solving paradigm that is different from other major A.I. approaches. A CBR system can be used in risk monitoring, financial markets, defense, and marketing just to name a few. CBR learns from past experiences to solve new problems. Rather than relying on a domain expert to write the rules or make associations along generalized relationships between problem descriptors and conclusions, a CBR system learns from previous experience in the same way a physician learns from his patients.
A CBR system will create generic cases based on the diagnosis and treatment of previous patients to determine the disease and treatment for a new patient. CBR systems can be built without the need of extracting knowledge from experts, which is difficult and requires time and expertise. The implementation of a CBR system consists of identifying relevant case features. A CBR system continually learns from each new situation. Generalized cases can provide explanations that are richer than explanations generated by chains of rules. To learn more, please click on the link below.

Case-Based Reasoning

Deep neural networks learn hierarchical layers of representation from the input to perform pattern recognition. When the problem exhibits non-linear properties, deep networks are computationally more attractive than classical neural networks. A deep network can be viewed as a program in which the functions computed by the lower-layered neurons are subroutines. These subroutines are reused many times in the computation of the final program. To learn more, please click on the link below.

Deep Learning/ Neural Networks

Unsupervised learning is learning from unlabeled data, where particularly informative privileged variables or labels do not exist. As a result, the greatest challenge is often to differentiate between what is relevant and what is irrelevant in any particular dataset. In the context of classification, the goal is to divide a set of unlabeled data into classes, or clusters. Unsupervised learning also encompasses dimensionality reduction, feature selection, and a number of latent variable models.
Traditional logic typically categorizes information into binary patterns such as, black/white, yes/no, or true/false. Fuzzy logic brings a middle ground where statements can be partially true and partially false to account for much of day-to-day human reasoning. For example, stating that a tall person is over 6′ 2″, traditionally means that people under 6′ 2″ are not tall. If a person is nearly 6′ 2″, then common sense says the person is also somewhat tall. Boolean logic states a person is either tall or short and allows no middle ground, while fuzzy logic allows different interpretations for varying degrees of height.
Neural networks, data mining, CBR, and business rules can benefit from fuzzy logic. For example, fuzzy logic can be used in CBR to automatically cluster information into categories which improve performance by decreasing sensitivity to noise and outliers. Fuzzy logic also allows business rule experts to write more powerful rules. To learn more, please click on the link below.


A business rule management system (BRMS) enables companies to easily define, deploy, monitor, and maintain new regulations, procedures, policies, market opportunities, and workflows. One of the main advantages of business rules is that they can be written by business analysts without the need of IT resources. Rules can be stored in a central repository and can be accessed across the enterprise. Rules can be specific to a context, a geographic region, a customer, or a process. Advanced Business Rules Management systems offer role-based management authority, testing, simulation, and reporting to ensure that rules are updated and deployed accurately. To learn more, please click on the link below.

Business Rules Management System

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from computer science, AI, databases, and operations research.
Brighterion’s constraint programming technology relieves programmers of the burden of learning a new language.
Genetic algorithms (GA) work by simulating the logic of Darwinian selection where only the best performers of a species are selected for reproduction. Over many generations, natural populations evolve according to the principles of natural selection. Those individuals most suited to the environment are more likely to survive and generate offspring, thus transmitting their biological heredity to new generations. A genetic algorithm can be thought of as a population of individuals represented by chromosomes.
In computing terms, a genetic algorithm implements the model of computation by having arrays of bits or characters (binary string) to represent the chromosomes. Each string represents a potential solution. The genetic algorithm then manipulates the most promising chromosomes searching for improved solutions. A genetic algorithm operates through a cycle of three stages:
1. Build and maintain a population of solutions to a problem
2. Choose the better solutions for recombination with each other
3. Use their offspring to replace poorer solutions.
Genetic algorithms provide various benefits to existing machine learning technologies such as being able to be used by data mining for the field/attribute selection, and can be combined with neural networks to determine optimal weights and architecture. To learn more, please click on the link below.