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Agentic Enterprise Intelligence.

More Reliable Predictions and Smarter Decisions for Business.

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Genius is designed to help machine learning professionals and data scientists

Build causal models that learn continuously in real time
Generate predictions that are more reliable, more explainable and more efficient
Accelerate time from data to decisions from weeks to days

Drag & Drop Interface*

An intuitive no-code graphical user interface designed to help you model the causal relationships between factors and variables.

*Bayes Nets only

Guided Workflows

User guidance to help you build models and run inference fast.

Build Domain-Specific Models

Upload CSV datasets to extract variables, factors and parameters and export as JSON to perform inference.

Continual Learning & Explainability

Train, measure and update explainable models continuously

Visualize Inference

Quantify uncertainty and express probabilistic predictions of likely outcomes.

Automated Binning

Group and categorize data in order to simplify analysis and optimize inference.

Graceful Exception Handling

Troubleshoot and debug errors, build resilient models and recover faster for a smoother development experience.

Model & Validation Tooling

Intuitive low-code user interface designed for modeling domain-specific causal models on which to perform reasoning, planning and learning using Active Inference.

Enhanced Inference & Learning

Advanced reasoning and planning using Bayesian inference mechanisms.

Model & Validation Tooling

Intuitive low-code user interface designed for modeling domain-specific causal models on which to perform reasoning, planning and learning using Active Inference.

Lifecycle User Enablement

Simplified deployment, tutorials and reference examples.

Enterprise Ready

Manage users, licenses, agents and support tickets.

Developer Friendly

Well-documented SDK and APIs along with customer success support.

Modeling & Validation Tooling

Intuitive low-code user interface for modeling and structuring cause and effect in data.

Performance Characterization

Command tool for monitoring model and inference performance in real time.

Enhanced Inference and Learning

Advanced Reasoning and planning using Bayesian inference mechanisms.

Lifecycle User Enablement

Simplified deployment, tutorials and examples for enabling users get time to value.

Easy to Install & Use

Kubernetes containers for easy deployments.

Analytics & Telemetry

Monitor user activity to enable data-driven decision-making.

Hallucinations

Drift

Static models

Sample inefficiency

Sensitivity to noise

Black box

Hardware inflexibility

Lack of uncertainty quantification

Goodbye

bots, pre-training, black box, fragile, oceans of data, energy intensive, tedious rework

Hello

agents, continual learning, explainable, flexible, sample efficiency, sustainable, rapid prototyping

Rapidly ideate and validate Bayesian models for inference that explicitly map causal relationships.
Build agents that respond to dynamic environments with online learning and planning based on real time observations and explainable decisions.
Streamline complex integrations and deployments.
ML Researchers can spend less time implementing and more time experimenting.
ML Engineers can spend less time adjusting models that fail to perform in the face of complexity and uncertainty.

Adaptable

Continual and online learning

Autonomous

Self-directed goal setting & decision making

Composable

Modular and reusable knowledge models

Efficient

Requires few samples and minimal compute

Explainable

Transparency into how predictions and decisions are made

Flexible

Powerful specialized GPUs are not required but optional

Interoperable

Shared knowledge means better decision making

Reliable

Predictions have confidence score qualifiers

Resilient

Fault tolerance and able to recover from failure

Scalable

Run in the cloud or at the edge

Sustainable

Less compute and less retraining means less energy

Quantify Uncertainty

Thrive in spite of of noisy, sparse or missing data

Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready.

Andy Tasker

Prodigii

Risk Assessment

Assessing the probability of financial default for a loan applicant.

Fault Diagnosis

Detecting system malfunctions in a manufacturing line.

Recommendation Systems

Personalizing movie recommendations for streaming services users.

Predictive Maintenance

Scheduling maintenance for a fleet of industrial machines.

Optimization

Finding the best delivery routes for logistics companies.

Resource Allocation

Distributing limited medical supplies during a health crisis.

Reliable

Predictions have confidence score qualifiers

Explainable

Transparency into how predictions and decisions are made

Sustainable

Less compute and less retraining means less energy

Spring 2025 Release Highlights

Genius Model Editor

Dozens of usability improvements including a model wizard to streamline building, validating, training and saving your models.
Models include Partially Observable Markov Decision Process (POMDP), Markov Decision Process and Discrete Bayes Net.
Continuous learning and latent/hidden variables are now supported
Model validation and testing
Enhanced inference and learning

Genius Customer Portal

Renamed Developer Portal to Customer Portal
More robust account administration including a dashboard and management of users, licenses, agents, models, tickets and more.
User analytics with telemetry.

Genius Hosted Services

We found that a minority of users required or had the technical skills to self-host so we are primarily hosting instances of Genius with all the benefits, security and assurances of a major cloud services infrastructure.

Genius provides familiar ways to ingest, validate, and preprocess data in preparation for creating machine learning models.

Genius provides tools to build, analyze, and validate probabilistic causal models of complex dynamic systems that can quantify uncertainty.

Deploy agents with your preferred tools and see your agent use active inference to reason, plan, and learn and act. They will continuously evolve with new observations and become specialized domain experts with ever-improving prediction accuracy and reliability.

	Starter	Professional	Enterprise
Monthly Price	Get Started Free $99 After Limited Time Trial	For Individuals $99 $199 After Limited Time Offer	For Organizations Contact for demo
Capabilities
Model Editor
SDK
API
Users	1	1	Unlimited
Inference Agents	1	3	Unlimited
Support	Community	Email	Priority
Hosting	Cloud	Cloud	Self
	Signup	Signup	Signup

Who is Genius for?
Our objective is to democratize Active Inference agents and Bayesian methods starting with supporting machine learning engineers who struggle with a model creation and deployment pipeline. As Genius evolves its features and capabilities will expand to include other personas. If you are looking for the following features and capabilities today then Genius was made for you.
- Intuitive tools for rapid prototyping and validation.
- Seamless integration with existing research libraries (e.g., PyTorch, TensorFlow).
- Advanced diagnostic capabilities to analyze model performance.
- Ability to create modular models that can be easily reused and adapted for future research.
- Reliable, scalable infrastructure for deploying models without needing extensive rewriting.
- Instrumentation and logging to understand model behavior in production.
- Support for retraining and fine-tuning models based on real-time data.
- Tools to efficiently update and deploy model versions.
- Advanced Bayesian modeling techniques for uncertainty quantification.
- A sandbox environment for fast iteration and debugging.
- Easy-to-use interfaces for testing and comparing different models.
- Visualizations for understanding model behavior and decision boundaries.
- Automated pipelines for model deployment, testing, and rollback.
- Easy integration with existing DevOps tools (e.g., Kubernetes, Docker, CI/CD systems).
- Real-time monitoring and alerting for drift and performance issues.
  Visual and interactive tools for troubleshooting and debugging.
What problems is Genius designed to solve?

Genius is ideal for tackling complex problems that require quantifying uncertainty and making inferences on the likelihood of causes given observed effects. Some examples:

Rain, Sprinkler, Wet Grass

What is the probability that it rained or that the sprinkler was on, given grass is wet?

→ P (rain | wet grass)

Cold, Flu, Cough

Given that someone has a cough, determine the probability that it is due to a cold or a flu or both.

→ P (cold | cough)

Infection, Viral Illness, High Fever

Given a high fever, determine the probability that it is due to infection or viral illness?

→ P (infection | cough)

Defective Machine, Power Fluctuation, Production Halt

Given that the production has halted, determine the probability that it's due to a defective machine, power fluctuation or both.

→ P (defective machine | production hault)

Agentic Enterprise Intelligence.

Problem

Solution

Who is Genius for?

Drag & Drop Interface*

Guided Workflows

Build Domain-Specific Models

Continual Learning & Explainability

Visualize Inference

Automated Binning

Graceful Exception Handling

Model & Validation Tooling

Headline

Enhanced Inference & Learning

Headline

Model & Validation Tooling

Enhanced Inference and Learning

Lifecycle User Enablement

Headline

Enterprise Ready

Headline

Developer Friendly

Headline

Lifecycle User Enablement

Enterprise Ready

Developer Friendly

Make sense of your data in minutes not weeks.

Risk Assessment

Assessing the probability of financial default for a loan applicant.

Fault Diagnosis

Detecting system malfunctions in a manufacturing line.

Recommendation Systems

Personalizing movie recommendations for streaming services users.

Predictive Maintenance

Scheduling maintenance for a fleet of industrial machines.

Optimization

Finding the best delivery routes for logistics companies.

Resource Allocation

Distributing limited medical supplies during a health crisis.

Beyond smart. Genius.

Spring 2025 Release Highlights

Research Spotlight

Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind

Who is Genius for?

What problems is Genius designed to solve?