Storage isn't just storage.
You're paying for every TB many times over - across backups, replication, audit copies, egress, cooling, and energy. The bill compounds quietly every quarter.
Jam storage engine
Store less. Restore with proof.
Jam turns folders, backups, logs, app installs, and repeated snapshots into verified archives. It tests the restore, compares storage modes, and shows the result before you delete or move the live copy.
Already in production
Trusted by customers, programmes, and partners.
Live revenue customers, healthcare and genomics channels, defence innovation pathways, and hardware ecosystem partners.
SwissVaultGenomics route
How it works
Most teams keep buying storage. Jam helps you decide what can move cold.
Storage isn't a one-time cost. Every TB you keep gets paid for again - in backups, replication, audit copies, network egress, and energy. Jam shrinks that footprint at the source.
Jam makes a checked archive.
Jam packages the selected data, records metadata, tests that it can be restored, and keeps the archive self-contained unless you deliberately choose reference mode.
We test on your data first.
Send a workload sample. Two weeks later you get a real compression ratio, restore speed, and dollar savings - yours to keep, with no commitment.
The archive modes
Pick speed, smaller files, or change-only snapshots.
The app explains these choices in plain language. Most users start with a self-contained archive. Backup and snapshot users can add a reference folder when they want Jam to store mostly what changed.
Best first choice.
Creates a verified archive quickly. Use it for backups, project handoffs, cold storage, and any archive that must restore on another computer.
Spend more time to save more space.
Best for logs, CSVs, JSON, source trees, telemetry, and structured folders where normal compression performs well.
Store mostly the change.
Use this when you have an older matching backup or snapshot. The archive can be tiny, but restore needs the original reference data.
Experimental, not default.
Jam's internal compressor is available for lab work, but today's app should recommend it only when the user is explicitly testing compression research.
Research lineage
Compression with serious academic roots.
Two papers we reference often: practical lossless compression with latent variables, and the broader thesis that compression reveals reusable structure in complex systems.
Practical Lossless Compression with Latent Variables using Bits Back Coding
The ICLR 2019 BB-ANS paper shaped Jam's early direction: lossless compression, latent-variable modelling, asymmetric numeral systems, and practical parallelization.
Read arXiv:1901.04866Compression is all you need: Modeling Mathematics
The 2026 preprint argues compressibility reveals reusable structure, hierarchy, and useful abstractions across complex knowledge systems.
Read arXiv:2603.20396Benchmark proof
A real local snapshot test, not a generic claim.
We tested Jam on real local Trading log data arranged as an older snapshot and a newer snapshot with a small changed tail. Every result below was restored and checksum-verified.
Repeated snapshot benchmark
Original folder: 53.0 MB. Source: local Trading logs with a small changed tail.
ModeArchive sizeSavedWhen to use it
Fast archive30.9 MB41.7% smaller
Quick, self-contained backup or handoff.
Smaller archive1.49 MB97.2% smaller
Text-heavy logs, CSVs, JSON, and structured folders.
Reference archive837.9 KB98.5% smaller
New snapshot compared with an older matching snapshot.
Dense reference11.7 KB>99.9% smaller
Near-duplicate snapshots where the reference remains available.
Reference archives are powerful for backups because they can store mostly what changed. Self-contained archives are safer when you want the archive to restore by itself on any machine.
Managed storage
Or let us run it for you. $5 per TB-month.
Roughly 78% below AWS S3 Standard list. S3-compatible API. SOC 2 operationally compliant. Pilots, restore drills, and workload reporting included. Pricing scope excludes request, retrieval, transfer, and tax.
Quick math
How much would you save?
Drag the sliders to your workload. List rates are public AWS S3 references; your real bill includes more line items.
USD $23,550
List-rate monthly bill
USD $1,750
On Cithorum at $5/TB-month
USD $21,800
Saved every month
93%
Reduction
Canada / ITB
Prime-ready Canadian content proof.
For primes with Canadian industrial benefit obligations: 94.54% CCV, six emerging-tech KIC mappings, four defence-domain competencies, and structured R1–R5 Value Proposition outcomes.
How it starts
Two weeks. From workload to dollar number.
Week 0
Briefing
30 minutes. We map your workload, current storage, restore needs, and any procurement context.
Weeks 1–2
Benchmark
We compress a sample of your data and report compression ratio, restore speed, and the dollar number - yours to keep.
After
Decide
Run Jam yourself, or move to Cithorum Cloud at $5/TB-month. No commitment until you've seen the numbers.