Lossy you can audit. A guaranteed error bound, with a certificate to prove it.
For scientific and sensor archives where a known tolerance is acceptable, Bounded mode shrinks data far past lossless while guaranteeing a maximum error you choose — and ships a tamper-evident certificate of the honored bound. Measured 4–80× smaller than lossless at a 1% bound.
You set the bound — it’s guaranteed
Choose a maximum absolute error (e.g. 1% of signal range). Every reconstructed sample is within that bound, by construction — not on average, but worst-case, on every point.
And it’s auditable
Each Bounded container embeds a tamper-evident certificate binding the honored error bound to the exact bytes via SHA-256. Anyone can recompute it and verify the promise was kept — the missing piece for regulated and scientific archives.
Still queryable & verified
Bounded files keep AT-1’s integrity trailer and in-place query. You get the dramatic size cut and a provable error guarantee without losing tamper-evidence.
Size vs. guaranteed error
Median across numeric/sensor tiers. The error bound held exactly on every tier tested — the auditable claim is real, not statistical.
| Guaranteed error bound | Median vs lossless | Standouts |
|---|---|---|
| 0.1% of signal range | ≈1.6× smaller | multi-channel 20× · genomic 9× |
| 1% of signal range | ≈4.3× smaller | multi-channel 83× · financial 16× |
| 5% of signal range | ≈11× smaller | most tiers >20× |
Compress to a 1% guaranteed bound
at1 optimize compress signal.npy signal.at1o --bound 0.01
at1 optimize verify signal.at1o # prints the max-error guarantee + certificate hashBounded mode is opt-in and scoped to numeric/sensor data. Lanes that require bit-exactness — medical imaging, forensic evidence, the AI Evidence Capsule, and model weights — remain strictly lossless and never use Bounded mode.