A single integrated AGI solution for ultimate scalability.

Our approach to AI is both dynamic and platform-agnostic, with universal I/O data formats that integrate directly with your current workflows. Our clients and developers love that our Artificial General Intelligence (AGI) platform scales with existing solutions vertically and horizontally with ease and little-to-no code or refactoring.

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AI is stuck.

Current AI platforms are built from outdated, dead-end approaches and aren’t evolving machine intelligence technology. You still can't solve all problems with a single solution. Top-down analytical platforms are fantastic at solving highly-specialized problems, but have to be manually modeled and maintained because they can’t learn autonomously from data. Bottom-up statistical models can be applied generally and can learn from data, but the learnings and solutions can’t be explained, leading to various implementation or ethical stop-gaps.

We're building the future of AI, together.

While most solutions today are engineered through aforementioned statistical and mathematical lenses, we looked to physics, information theory, and neuroscience to model cognitive processors that emulate well-known intelligence functions instead of inefficient and unknown brain mechanisms. This resulted in our Genie™, a fully explainable universal platform capable of solving any data-centric problem in any environment that can learn from data and accept expert input.

An explainable, deterministic approach to delivering integrated AI solutions.

We've separated the implementation layers–Data, Intelligence, and Application–to allow them to be addressed individually by different groups with varying expertise within a team or organization.
Here's how we break it down:


Our universal data input format–GDF™ (GAIuS Data Format)–is a three-field JSON object used to input any data set into the system. Configured manually or via a simple API integration, your input data becomes a question for the intelligence system to answer, built as a sequence of events containing strings, vectors, and determinants. Because your data is decoupled from the intelligence and application layers of the system, input data sets can change while the Genie agents, which house the cognitive processors and manipulatives, and integration can all remain the same, allowing for ultimate scalability.


The separate intelligence system is built from two primary components. The Genie™ Platform is our GUI for data management, application integration, and configuring our proprietary GAIuS™ Framework–General Artificial Intelligence using Software. Together, Genie™ and GAIuS™ enable engineers at all levels to design solution genie agents through a graphical topology of Primitives–wrappers for cognitive processors (CPs) and data manipulatives that manage network connections, API calls, databases, and processing. Genie agents can learn in real-time to accelerate improvements, particularly when complexity increases. Once configured, the intelligence system provides a series of universally formatted Prediction Objects, which it can actively assess to determine the best course of action, maximizing the predicted utility value of outputs.


With only four simple REST API calls necessary to integrate into current workflows and future domains, our platform-agnostic framework enables endless opportunities for application. Because this layer is also separate from others within the stack, minimal effort or skill is required for integration or scale. Easily increase the complexity of a problem without needing to update or change code by keeping the same REST interface while you manually or automatically evolve the genie. Because of the universal I/O data formats, you can also apply the same genies to new problem domains without any pre-processing or pre-modeling. Simply change the data set. The best part? All solutions are explainable.

A perfect pair.

Though our platform consists of multiple layers, it is the Genie™ Platform and GAIuS™ Framework that are responsible for real-time learning, fully explainable results, domain adaptation, and universal I/O. We built Genie–our GUI and IDE–to eliminate complex integrations, time-consuming development, and difficulties in deploying rapidly applying scalable solutions. When working in Genie, you'll visually design solutions (Genie agents) by modeling and manipulating topologies of how data flows through the cognitive processors within GAIuS–the cognitive AI operating system that provides explainable results and learns in real-time to improve solutions automatically.

Solve Any Problem.

Build, scale, and improve faster with fewer mistakes. With solutions able to automatically evolve within GAIuS, problem complexities can increase without needing additional modeling. And in case you do want to manually iterate solutions (who doesn't love a little control), hop into Genie to visually re-model in a few simple steps. Because we keep data, intelligence, and application layers separate and employ universal I/O data formats, you're able to use the same solutions for different data sets and applications, and team members can work on different layers concurrently. No more solving every problem. Solve any problem.

Meet our tech family.


The graphical user interface and integrated development environment for designing and deploying solutions through GAIuS.


The core kernel of the intelligence system where data flows through cognitive processors and manipulatives and outputs prediction objects.


GAIuS Data Format. Our three-field JSON object universal data format through which any data set can be input into the system–manually or automatically through simple API integrations.

Answers you've been looking for.

In order to solve problems in any environment from any data set, we needed the platform to offer a multitude of answers within Prediction Objects produced by GAIuS:


What classification or category does X belong to?


What has and/or will happen in the past, present, and future?


What anomalies, utilities, and missing or extra events are in the data?


How do I iterate or change X to make it better?


For this event and context, what is the best course of action?


Why did this particular part break down in the machine?


What's the probability of X happening next?


What patterns exist in the data?


What does X mean from a historical or analytical perspective?

Try on your own, or together.

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