Architecture

Coelanox is not a training platform and not a generic MLOps suite. It is the layer that makes inference auditable: you ship a sealed .cnox artifact, verify integrity before execution, and run through a small deterministic executor—with optional per-op logging for evidence at the compute level.

What Coelanox is not

  • Not a replacement for PyTorch or TensorFlow for research
  • Not an explainability overlay—audit is from the operations that actually ran
  • Not a catch-all “AI governance” product—it is infrastructure for verifiable inference

Container (.cnox)

A single binary: header and integrity (e.g. SHA-256 over the payload), manifest, Universal IR, compressed weights, optional CLF kernel blobs. Modify any byte and verification fails before the runtime executes.

Runtime

Turing-incomplete executor: fixed plan, kernel dispatch, no dynamic codegen in the hot path. That is a security and auditability property. Scalar and accelerated backends sit behind the same contract; correctness and audit trails come first in the roadmap story.

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Technical thesis (narrative) · Interactive technology page (pipeline, CLF, deployment shapes)