Predictive Analytics For

Insights Into The Future

Decision Power Today for a More Confident Tomorrow

Consult Us Today

Predictive Analytics For

Insights Into The Future

Decision Power Today for a More Confident Tomorrow

Consult Us Today

Providing Access to Advanced Analytics at Your Fingertips

RWISE Reference World Information and Simulation Environment 4.0

The most powerful combination of data analytics, AI, ML, and Agent-based Societal modeling & Simulation available

The Reference World Information Synthetic Environment (RWISE) platform is a decision-making tool providing reliable, actionable information and data-driven insights that allow leaders to test policies, identify shortfalls, examine multiple parallel simulations, and compare key metrics of proposed courses of action before committing resources.

Reference World is more than a data analytics or Modeling & Simulation software. It is an AI/ML/DS enriched workflow that moves data first through curation, extract, transform, load, then through AI/ML capture and creation of a relationship network model. All relationships within and between data sets are integrated into a synthetic model. We provide an AI/ML enriched Simulation engine where you can test the future impacts of changing variable based on the synthetic models.

Changing the Game

Bigger DATA, Better ALGORITHMS, Better RESULTS
All Predictive modeling platforms have the same challenges when it comes to the reliability of data and the reliability of algorithms used to analyze it, Right? Think Again

Introducing RWISE! Seeing is believing

In its fourth generation, RWISE offers the most intelligent process for Data Curation, Intelligent Integration of Algorithms, and Agent-Based Modeling. Its proprietary process addresses normal statistical biases that have corrupted and degraded the reliability of competitive architectures. AND we’ve been doing this for a while!

HOW IT WORKS

STEP ONE

The Ingestion Process (“Human on the Loop”)

Making the data the best it can be
Curating raw data using Machine Learning to translate it to a common ontology
We can ingest data from virtually any source.
Our capacity is completely elastic and capable of updating in real-time. That means social media, government statistics, polling, public opinion, or anything else. This allows you to see the effect of modeling as it happens, not days later.
We understand that sources, timing, and reliability of data are the foundation of reliable forecasting.

STEP TWO

Building the Neural Network
The input of curated data (“Human on the Loop”)

The curated data workflow is designed to reduce the statistical biases common to AI/ML systems. The output is a neural network of relationships where small subgroups remain rather than being lost as statistical noise. The resulting synthetic model includes all data relationships so all queries have already been answered.

RWISE then begins to build the neural connections that are the foundation for the modeling of complex impacts of events. Unlike virtually all other platforms, RWISE integrates multiple technologies in the neural development phase. This makes data a more global in its use during simulations. Unlike others, we can capture the impact of different perspectives.

STEP THREE

Agent-Based Modeling (“Human in the Loop”)

Determining the coefficient baseline of the future
Apply Variable Conditions and Changes
View Impact on Future Performance/Results

The Algorithms

Our advantage over other solutions
We design our analytics using multiple AI processes. methods including Boosted Decision Trees, Logistics Regression, and Gradient Descent are part of our workflows.

No one else integrates at the level of sophistication like RWISE does.

Creating a baseline
The RWISE simulation engine can carry historical trends forward to create a baseline future. You can then insert different actions to see their likely future impact.

You can escape the flaw of backward-looking approaches to forward problems. Emergent phenomenon are able to emerge.

What We Do Thru RWISE:

Perform AI/ML Enriched Agent-Based Modeling & Simulation
We layer the synthetic fabric of facets of societies from multiple sources with structured and unstructured data.
Populate the synthetic world with cognitively and socially sophisticated agents.
Agents respond to and act on external signals and stimuli based on underlying logic for the agent type.
Forecasts societal changes at selected levels of granularity use both deductive and inductive logic - allowing new patterns to emerge.

Why Choose RWISE?

The Most Advanced A I Platform Available Today
Our synthetic models mimic the human brain to forecast Changes in Human Behavior.

RWISE Uses Multiple Machine Learning Technologies
Greater Intelligence Applied to Complex Machine Learning

The Power of Both Inductive and Deductive Modeling
Competitive platforms focus on Deductive modeling. (Generally working off of legacy assumptions)

Both "Human in the Loop" as well as "Human on the Loop"
This provides greater flexibility in manipulating the future environment. Automated process with human intervention as needed.

Agent-Based Modeling
Change the conditions to see the impact of future actions.

Mimic the Human Brain

BUILDING THE SYNTHETIC ENVIRONMENT
DATA INGESTION AND CURATION
Data is ingested into the RWISE platform, where it is curated and securely stored.

CREATING THE NEURAL CONNECTIONS
RWISE creates a neural network much like the human brain.