The current AI paradigm suffers from a critical inefficiency where fine tuned intelligence is rigidly locked to the specific architecture of a single neural network. This rigid coupling creates a brittle ecosystem where every infrastructure update forces organizations to discard their accumulated knowledge and restart development from a blank slate.
We are architecting a Universal Adaptation Layer to fundamentally sever the link between specialized knowledge and the models that host it. We view the foundation model as a transient vessel while preserving your learned adaptation as a durable and independent asset.
Our research identifies the core functional similarities between divergent models to create a seamless bridge for porting complex behaviors without retraining. This allows a specialized reasoning pattern developed on one model to project directly onto a completely different model while maintaining full fidelity.
We are defining the inevitable standard for model interoperability by creating a persistent memory layer for the entire AI stack. In this new paradigm the model architecture becomes ephemeral while the intelligence remains permanent.