Data
Cipher-Grade Encoding
Our universal encoded data format keeps information secure at all times. You can store it, move it, and execute AI training and inference on it without decoding to plaintext. When a plain copy is required, decoding is bit-perfect and verifiable.
The protection is structural, not peripheral: a core part of data transformation, not an additional pipeline step with independently-vulnerable orchestration. The encoded form resists inspection and alteration, and keeping data in its encoded form means fewer places where plaintext exists on disk, in memory, or on the wire.
Integrity checks are built in, so every restore is proven, not assumed. Operationally, this simplifies hardening: standard policies (key/seed handling, access controls, logging) can be applied to the encoded artifact itself, rather than scattered across multiple decoded intermediates.
Pipelines become cleaner. Common steps that exist solely to prepare or sanitize plaintext are minimized or removed, because the encoded representation is already uniform and computable. Data follows a single, deterministic path: ingest → encode → compute → (optional) restore. For portability, files can be made self-extracting so that restore remains possible on systems without prior support.
End-to-end encoding does not change what the data means or how precisely it restores; it changes where, and for how long, plaintext exists. That shift—shorter lifetimes, narrower surfaces—produces immediate security and compliance benefits without forcing a rewrite of existing models or tools.
Our data stays unreadable by default. At rest, in transit, and in use, the artifact remains sealed while your systems store it, move it, and even execute AI inference on it. When a plain copy is required, restore yields a bit-perfect original and proves integrity on the way out.
The protection is structural, not peripheral. The encoded pattern is designed to resist inspection: content is distributed and opaque, so adversaries cannot map local changes in the package to directed changes in the underlying data. Any attempt to manipulate the payload must coordinate across the entire pattern and will fail verification. Plaintext exposure is minimized because decode is only invoked on demand, at the narrowest points of control.
Operationally, this reduces risk without forcing a rebuild. Existing models and pipelines can run on the sealed form; standard controls (keys/seeds, access, logging) apply to a single artifact instead of a trail of decoded intermediates. Integrity checks are built in, so restores are proven, not assumed. For portability, files can be made self-extracting so authorized restores remain possible on systems without prior support.
Data
Compute
Portable across CPUs/GPUs/NPUs/embedded
Compute
Friendly Run where networks are constrained or absent
Compute
Compute
Adopt without retraining; preserve outcomes
Compute
Materially fewer prep stages vs baseline
Compute
Inference and fine-tuning without a decode step
Data
Data
Fewer rotations/rewrites; resilient short of catastrophic loss
Data
Leaner movement and comparison with built-in verification
Data
Data
Recover through real-world corruption within defined bounds
Data
Smaller files with bit-for-bit, verifiable restore.