Recursive, self-improving infrastructure for scientific research.. autonomous labs.. discovery at scale.

The path to accelerated scientific discovery is to scale in-context recursive learning that works with any model, any experimental harness, and across all research disciplines. As scientific models proliferate and the frontier becomes multi-domain, experimental context and operational tooling should be separated from fragile one-off scripts and compound continuously.

Operational overhead is no longer a constraint.

Pre-training and post-training, scaling on data and compute, will continue to push foundational models' capabilities, but will not enable them to learn, adapt, and improve experiments in real time.

We build all the hard infrastructure parts of a scalable research engine, with dense, interconnected, compounding learnings, and an understanding of scientific lineage.

Our architecture helps research teams continuously improve by extracting and synthesizing context from papers, codebases, and experimental iterations: injecting verified context into autonomous agents in real time.

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