Scale your tacit knowledge

Our Dynamic Learning Approach

Using custom contextual embeddings we create latent mathematical representations of tacit domain knowledge.

We use these representations to seed models that continuously learn by dynamically recalibrating neural architectures on the fly with minimal human feedback.

Our platform allows us to solve an entirely new class of real world problems by overcoming sparse training data, computational inefficiency and catastrophic forgetting. The result is human-level accuracy, minimal model training, tuning and maintenance costs, and higher value predictive insights.