Brainpower
for Agents.

Genius™ empowers agents with cognition, the ability to reason, plan, learn.

Beta Signup

According to Albert Einstein there are 5 levels of intelligence with simple being the highest form.

Genius enable agents to ascend this intellect ladder by becoming experts that deeply understand and optimize complex dynamic systems in order to make things simple.
2024-12-14 Intelligence Ascending
Simplicity is the ultimate sophistication.
Leonardo da Vinci

What are Agents?

Agent

Based on pioneering neuroscience and the Active Inference framework, agents are intelligent systems designed to interact with their environment by actively gathering information and making decisions based on their internal (generative) model of the world.

Agents aim to minimize "surprise" by constantly predicting and updating their understanding of the environment through actions it takes, essentially acting to confirm its predictions and reduce uncertainty.

This approach is rooted in Bayesian inference principles and is often compared to how biological organisms navigate their surroundings.

Model

An agent requires a generative model in which to store domain knowledge or expertise about its environment.  Think of this internal model as a 'cognitive map' representing context-dependent relationships and the cause-effect structure of the environment.

Agents continuously perform inference on this model in order to generate increasingly more accurate predictions and select optimal actions.

Reason

Run inference on the generative model in order to deduce the likelihood of a cause or effect based on past data.

Plan

Run inference on the generative model in order to deduce the likelihood of a cause or effect in the future.

Learn

Continuously update the generative model in a real time feedback loop based on observations of cause and effect.

Act

Interact or intermediate with knowledge domains, systems, devices, machine learning models and even other agents.
Genius Logo White

Grounded in neuroscience, Genius is a suite of tools for designing autonomous intelligent agents that continuously reason, plan, learn, and act.

Intelligence Powered by Genius

Agents powered by Genius have agency and autonomy can act as the intelligent interface to knowledge repositories, systems, devices, other AI and ML models and even other agents.
2024-12-14 Agent Tools

Unlimited Potential

Transcend the limitations and shortcomings of conventional machine learning.
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

Instant Insight

Make sense of your data in minutes not weeks.
  • 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.
2024-11-27 Genius documentation
2024-11-27 Genius Model Builder

Sensible Intelligence

Genius enables agents to provide results that are highly effective, efficient and auditable.
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

Reliable

Predictions have confidence score qualifiers

Explainable

Transparency into how predictions and decisions are made

Sustainable

Less compute and less retraining means less energy

The Right Answer

When you need the right answer at the right time for the right context, your agent needs Genius level intelligence.

Intelligence Powered by Genius

If you're developing agentic software, solving complex problems with high uncertainty, or working with limited data and compute but need reliable results, Genius was designed for you.

Use Cases

The correct, optimal or ideal answer to a question is often not absolute and may vary depending on context and circumstances.  These factors and variables must be considered when making predictions and decisions and the answers should be qualified with degrees of confidence or certainty.

When good enough isn't good enough

Today's machine learning models and frameworks are incredibly useful at certain things but when capabilities like the ones below matter, your agent needs Genius level intelligence.

Risk Assessment

Assessing the probability of financial default for a loan applicant.

Predictive Maintenance

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.

Features & Benefits
Modeling & Validation Tooling

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

Genius Agents

Simplified & high-performing standard agent that can run based on any compatible models.

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

User Analytics & Telemetry

Know what features our beta users are using based on 3rd party analytics / telemetry tools to monitor agent health so that we know what and how to monetize.

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.
Logo Prodigii
Andy Tasker
Prodigii

Streamline Your Pipeline

Simplify and automate from planning to production.

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.

Something About Agents

Not sure we really need this but it does provide substance

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

2024-12-11 Genius Agent Diagram
Genius Runtime | Simple & Performant

Architected to reduce complexity, improve inference efficiency, and support experimental models, Genius Runtime enables quick integration and deployment in production.  Customer value is in running inference and planning in a fully supported Genius Runtime, which powers the ability to turn observations into actions and surface the reasoning behind them.

Genius Model Builder | Intuitive Low-Code UI

Intuitive tooling for building and validating models, supporting rapid experimentation with a "1-click-to-value" workflow—ideal for researchers exploring new ideas and engineers fine-tuning production models.

Genius Agents | Optimized Bayesian Inference

Design agents that deliver fast and accurate predictions and action selections while ensuring models are performant and efficient even with very limited data or changing environments.

Lifecycle Management | Viva la Evolution

Monitor and curate which agents should flourish or perish as they evolve.

User Instrumentation | Deep Model Insights & Analytics

Early-stage instrumentation to track user interactions, model performance, and workflow bottlenecks, and to aid with continuous refinement and optimization.

Developer Success | Get up to Speed Fast

Streamlined installation, documentation, tutorials, reference examples and a Discord community to help you quickly master complex concepts and workflows like hierarchical modeling, planning, and uncertainty quantification.

Risk Assessment

Assessing the probability of financial default for a loan applicant.

Example: A bank uses a Discrete Bayes Net to evaluate the risk of a client defaulting on a loan by considering factors like credit history, income stability, and existing debts. The model combines these probabilistic inputs to provide a clear risk score for decision-making.

Risk Assessment

Fault Diagnosis

Detecting system malfunctions in a manufacturing line.

Example: A POMDP is used to identify faults in a production system where not all conditions can be directly observed. The model accounts for hidden states (e.g., internal machinery wear) and observable data (e.g., temperature, vibration), allowing for sequential decision-making to diagnose potential failures efficiently.

Fault Diagnosis

Recommendation Systems

Personalizing movie recommendations for streaming service users.

Example: An MDP can be applied to optimize a recommendation system that learns user preferences over time. By observing user interactions and feedback, the model decides on the best content to recommend next, maximizing user engagement. For practicality, a starting point would encompass a coarse-grained method on large timescales.

Recommendation Systems

Predictive Maintenance

Scheduling maintenance for a fleet of industrial machines.

Example: A Discrete Bayes Net can model the probability of machine failure based on historical data and sensor readings. This helps predict when maintenance should be performed to minimize downtime and extend equipment life.

Predictive Maintenance

Optimization

Finding the best delivery routes for logistics companies.

Example: An MDP is used to optimize routing decisions for a delivery fleet by modeling different states such as traffic conditions, fuel levels, and delivery deadlines, aiming to minimize travel time and fuel costs. This is dynamic route optimization based on changing circumstances or non-stationary probability distributions.

Optimization

Resource Allocation

Distributing limited medical supplies during a health crisis.

Example: A POMDP can be used to allocate resources like vaccines or emergency medical kits. The model helps make informed decisions by considering uncertain demand, supply availability, and potential outcomes, ensuring that resources are deployed where they are most needed. Explainability is key for accountability and trust.

Resource Allocation

FAQ

FAQ

Brainpower

for Agents.

The missing link in the AI stack, Genius Agents™ are the

Brainpower, Cognition, Smarts, Wits, Intellect, Cerebral Cortex, Executive, CEO, Captain, Commander, Pilot, Driver, Maestro, Orchestrator, Ringmaster, Puppeteer, Director, Strategist, Driver, Shot Caller, Decision-maker

behind your app that reason, plan, learn, and act.

Beta Signup

87% of AI projects fail

According to Venturebeat, there are many technical reasons why most machine learning projects don’t make it into production.
Time-Consuming

Experimenting with new techniques takes a long time due to complex setups and tuning.

Operationalization Gap

Models often don’t translate smoothly from research to production, leading to wasted effort.

Scalability

Adapting and scaling experimental models to new, larger datasets is often a bottleneck.

Tooling Complexity

Current research tools can be fragmented, requiring constant switching and manual intervention.

Representing Uncertainty

Current research tools cannot adequately represent uncertainty in predictions nor flexibly and adaptably handle noisy data.