Data
Noise & Decay Robustness
Built for the real world. Files stay decodable and verifiable on aging media, flaky networks, or noisy sensors. With up to 15% random noise or 7.5% total loss, decoding still returns a bit-for-bit original with proof.
Why it survives. Like a hologram, we spread information across the whole file. Local damage does not cause local loss. Random errors are dispersed and cancelled during decode, so the exact original comes back and verification confirms it.
Grace under faults. Raw layouts and many compressors fail unpredictably when bits flip. Our structure degrades gracefully and keeps working. Within limits, you can keep storing, moving, and even compute on the damaged file, then run a verified restore when you choose.
Operational gains. Fewer emergency rewrites, fewer rotation cycles, calmer behavior under imperfect conditions.
Tamper resistance. Targeted edits effectively fail. To force a specific change, an attacker would need coordinated edits across the entire pattern. Those attempts are absorbed by the noise tolerance and rejected by verification.
More on
Data
Compute
Cloud-Optional & Silicon-Flexible
Portable across CPUs/GPUs/NPUs/embedded
Compute
Edge-Capable, Offline & Air-Gapped
Friendly Run where networks are constrained or absent
Compute
Compute
Transparent Wrapper for Existing Models
Adopt without retraining; preserve outcomes
Compute
Significantly Less Preprocessing
Materially fewer prep stages vs baseline
Compute
Direct Execution on Encoded Data
Inference and fine-tuning without a decode step
Data
Data
Archive-Grade Durability
Fewer rotations/rewrites; resilient short of catastrophic loss
Data
Fewer Replicas & Lower Sync Bandwidth
Leaner movement and comparison with built-in verification
Data
Data
Data
Guaranteed Lossless Compression
Smaller files with bit-for-bit, verifiable restore.