Benefit 01

Encoded at rest, in transit, and in use

Benefit 02

Fewer decode points; reduced plaintext exposure

Benefit 03

Verifiable, bit-perfect restore on demand

Benefit 04

Cleaner pipelines with fewer plaintext intermediates

Benefit 05

Optional self-extracting files for portability

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.

More on

Data

Compute

Cloud-Optional & Silicon-Flexible

Portable across CPUs/GPUs/NPUs/embedded

Cloud-Optional & Silicon-Flexible

Compute

Edge-Capable, Offline & Air-Gapped

Friendly Run where networks are constrained or absent

Edge-Capable, Offline & Air-Gapped

Compute

Material Efficiency Gains

Up to ~3x lower compute and power*

Material Efficiency Gains

Compute

Transparent Wrapper for Existing Models

Adopt without retraining; preserve outcomes

Transparent Wrapper for Existing Models

Compute

Significantly Less Preprocessing

Materially fewer prep stages vs baseline

Significantly Less Preprocessing

Compute

Direct Execution on Encoded Data

Inference and fine-tuning without a decode step

Direct Execution on Encoded Data

Data

Self-Extracting Files

Restore anywhere with an embedded <250 kB decoder

Self-Extracting Files

Data

Archive-Grade Durability

Fewer rotations/rewrites; resilient short of catastrophic loss

Archive-Grade Durability

Data

Fewer Replicas & Lower Sync Bandwidth

Leaner movement and comparison with built-in verification

Fewer Replicas & Lower Sync Bandwidth

Data

General Feature Vector

One model-ready representation across data types

General Feature Vector

Data

Noise & Decay Robustness

Recover through real-world corruption within defined bounds

Noise & Decay Robustness

Data

Guaranteed Lossless Compression

Smaller files with bit-for-bit, verifiable restore.

Guaranteed Lossless Compression