from a 1.1 GB bundle, beating the completed best baseline by 43.28 MB.
98.8%
raw footprint reduction on a 1.1 GB compact JSONL validation bundle
76.8%
smaller than the completed best baseline on that compact JSONL lane
99.0%
raw footprint reduction on a 256 MB sharded NDJSON validation bundle
14/14
world baselines completed on multiple headline structured-log runs
Brag Sheet
Wins worth taking to a storage budget meeting.
ZJX is built for the places where archive volume piles up quietly and turns into recurring spend. The front-page story is simple: selected structured-data lanes are already showing verified size wins against serious comparators.
from a 1.25 GB local telemetry row, smaller than tar.zst-long-22 by 3.84 MB.
from a 256 MB validation bundle, 38.4% smaller than the strongest completed comparator.
smaller than zpaq-m5 on a generated developer workflow artifact bundle.
Storage Economics
The win is not a prettier archive. The win is a smaller bill.
When eligible structured archives land near a 1-2% footprint, the budget conversation changes. ZJX gives infrastructure leaders a focused path to test those lanes before expanding retention, telemetry, and compliance storage.
Example eligible archive
100 TB
ZJX-style target footprint
1.7 TB
Billable storage removed
98.3 TB
At $20/TB-month
$1,966/mo
Raw telemetry
Strong baseline
ZJX result
Representative local telemetry row: lower retained bytes mean lower storage pressure and more room to preserve useful history.
Executive Story
For leaders who need savings, not another compression lecture.
Storage spend
Reduce retained bytes on structured archive lanes before they become multi-year cloud and backup costs.
Retention leverage
Keep more useful operational history without letting log growth dominate the infrastructure budget.
Validation first
Run controlled enterprise trials with restore checks, baseline comparisons, and buying-team evidence.
Who Should Look
ZJX belongs in front of teams with expensive structured data.
Infrastructure
Telemetry growth, archive sprawl, backup windows, and recurring object-storage bills.
Data Platforms
High-volume JSONL, NDJSON, event streams, generated traces, and compliance-retention datasets.
Finance
A direct savings case: fewer retained bytes, lower run-rate pressure, and measurable comparator wins.
Private Enterprise Validation
Bring the archive lane. ZJX will bring the comparison.
Greyforge Labs is prioritizing qualified enterprise validation for teams with large structured archives, meaningful retention costs, and a clear need to reduce storage footprint.
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