Offline AI for government labs.
The latest AI models on your own hardware, with every dataset staying inside your lab.
Let GEM handle the data while your team does the research.
Built into VaultOS, GEM can sort test data, search decades of technical reports, port legacy code and draft program reports, with every file staying inside your lab.
Go through last quarter's test runs on Rig 3 and flag any that drifted outside spec.
Read 128 run logs, checked spec limits, flagged 18 runs
18 runs drifted past the +4% limit on thermocouple T4, all starting two days after the June 12 recalibration. The rig likely needs to be recalibrated again.
Find every internal report since 2005 that tested composite panels above 400°C and tell me what failed.
Searched 18,600 reports, found 14 matches, compared the failures
14 reports match. Most failures were delamination above 450°C, traced to one resin lot. A 2021 retest with new resin passed.
Thermal cycling, panel set B
“Edge delamination at 480°C after 12 cycles, starting at the bond line.”
Delaminated
Resin supplier study
“Failures trace to resin lot R-7 from one supplier, used in both test sets.”
Delaminated
Retest with new resin
“Panels with the replacement resin held to 455°C for 40 cycles.”
Passed
+11 more reports
Port our Fortran heat-transfer model to Python and confirm the results match the original.
Read 14,200 lines, translated 38 routines, ran 50 test cases
Ported and verified. Every routine is translated, and all 50 test cases match the original Fortran results.
heat_model.f90
subroutine solve_conduction(T, k, dx, n) real(8), intent(inout) :: T(n) do i = 2, n-1 T(i) = T(i) + k*(T(i+1) - 2*T(i) & + T(i-1)) / dx**2 end doend subroutineheat_model.py
def solve_conduction(T, k, dx): # vectorized, same math as the # Fortran loop over i = 2..n-1 T[1:-1] += k * (T[2:] - 2 * T[1:-1] + T[:-2]) / dx**2 return T✓50 of 50 test cases match·max difference 0.0007%·runtime 41 min → 6 min
Draft the Q3 progress report for the program office from this quarter's notebooks and run logs.
Read 212 notebook entries, pulled 6 experiments, drafted 9 pages
Q3 report drafted. Two of four milestones are on track. The other two slipped, and each is flagged with the reason and a new date.
Everything your lab needs, in one box.
Run the models your lab approves.
- Reasoning model Open source 70B Loaded
- Language model Open source 120B Loaded
- Small model Open source 24B Ready
- materials-lab-v3 Your lab 70B Fine-tuned
VAULTSHIELD · INCOMING
- Malware scan
- Hidden instructions
- Data labels
Every file is checked before it gets in.
TODAY
41,280questions
12 AMTOKEN CHARGES$0
Use it all day, with no meter running.
Your research stays on your network.
The Cube sits on your lab's own network. Files, models and answers never pass through anyone else's servers.
- Researchers connected214
- Files indexed1.2M
- Models stored on the Cube6
- Lab files sent outside0
Your team's AI, owned outright.
- Lab data is processed on site.
- No token bills, however hard you run it.
- Models you fine-tune belong to your lab.
- Link more Cubes as new teams come on.
Common questions.
Yes. Load the open models your lab approves, including ones you've fine-tuned on your own data.
No. It runs on your lab's internal network. If you choose to connect it, it's over wired ethernet only, with no wireless connection at all.
Before a new file is added to the Cube, VaultShield inspects it.
It's a purchase, and costs don't climb with use, so you can plan around it.
An Alpha Cube serves up to 600 people at once, and more Cubes can be linked as teams grow. Compare models
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AI that works inside your agency.
Built for work that can't run in the public cloud.
