Structure knowledge using artificial agents

We apply ontologies and computational semantics to generate structure across each step of the knowledge production process: data cleaning, chunking, embedding, pretraining, fine tuning, inference, prompting.

Models perform better on pretrained agentic tasks and inference in domain specific instructions.

We build artificial agency by procedurally generating training and fine tuning of base models to proliferate task competency in a structured manner while also at scale.