Symbolica

The Next Dimension in Structured Machine Learning

Using a powerful mathematical toolkit based on category theory, we move beyond next token prediction towards true structured reasoning.

Approach

All current state of the art large language models such as ChatGPT, Claude, and Gemini, are based on the same core architecture. As a result, they all suffer from the same limitations.

Extant models are expensive to train, complex to deploy, difficult to validate, and infamously prone to hallucination. Symbolica is redesigning how machines learn from the ground up.

We use the powerfully expressive language of category theory to develop models capable of learning algebraic structure. This enables our models to have a robust and structured model of the world; one that is explainable and verifiable.

It’s time for machines, like humans, to think symbolically.

Model

Structured (Co)Inductive Reasoning

By building models which reason inductively, we tackle complex formal language tasks with immense commercial value: code synthesis and theorem proving.

A New Era of Compliance and Interpretability

Symbolica is working to eliminate unstructured model outputs and hallucinations.

We're aiming to allow developers and end users to understand and specify how and why model outputs were produced.

This interpretability and control over model outputs - including the ability to delete proprietary information from the training set - is imperative for mission-critical applications.

Accelerating Time to Market

By baking in structure into inputs, outputs, and reasoning, Symbolica models are significantly more data-efficient than traditional unstructured methods.

Our models can be trained using smaller data sets, faster, with order-of-magnitude improvements in inference speed.