ARCHAEUS

Organizing intelligence from first principles

We're building the next generation of language models through proprietary training methodologies that fundamentally improve how AI systems internalize and reason about knowledge.

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Our Research

Archaeus is developing graph-structured training methodologies that enable language models to achieve superior knowledge internalization and relational reasoning. Our approach restructures how training data encodes information — transforming unstructured text into rich knowledge representations that models can learn from more efficiently.

2.4x Improvement in vocabulary internalization vs. standard training approaches
p < 0.001 Statistical significance across controlled experiments
d = 1.07 Large effect size (Cohen's d) demonstrating robust methodology

Our Approach

Knowledge Graph Construction

We transform raw web content into structured knowledge graphs that preserve semantic relationships, entity hierarchies, and contextual connections that flat text discards.

Structured Training Data

Our proprietary pipeline converts knowledge graphs into training-optimized formats that enable language models to internalize domain knowledge with unprecedented efficiency.

About Archaeus

Named after Paracelsus's concept of the invisible organizing force that transforms raw matter into structured life, Archaeus embodies our mission: bringing structure to the chaos of unstructured data to create more intelligent AI systems.

Founded in 2026. Based in St. Petersburg, Florida.