Compute
Transparent Wrapper for Existing Models
Adopt the encoded pipeline without touching your weights. The wrapper presents a model-compatible interface, feeds batches from encoded stores, and returns outputs in the formats your systems expect. You keep your models, checkpoints, and tooling; the wrapper handles the translation and execution on the encoded form.
Integration is straightforward. Drop the wrapper at the I/O boundary (loader → model → writer) and keep your training and inference code paths intact. Because the mapping is deterministic and outcomes are consistent with plain-data runs, you can A/B, canary, and roll back using your existing test harnesses and metrics.
Operationally, this preserves hard-won investments. Frameworks, serving stacks, feature stores, and observability pipelines stay in place. Teams migrate incrementally—one service, one job, one workload at a time—without a destabilising retrain cycle.
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Compute
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
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
Noise & Decay Robustness
Recover through real-world corruption within defined bounds
Data
Guaranteed Lossless Compression
Smaller files with bit-for-bit, verifiable restore.