Send
What you send
- Model card or release notes
- Eval reports and launch threshold
- Known failures and regression runs
- Export terms or handoff constraints
App Data / Models
Available nowTier 1
Model Launch Check
Check one model release before launch.
Send a release candidate with eval reports, known failures, regression runs, and export terms. The app keeps the launch criteria, blockers, and handoff files in one place.
Ready to scope now. Start with one real batch and get back the output your team can use.
Send
model release candidate
Run
Model Launch Check
Get
Model launch file
App path
Model Launch Check
Available now
InputRelease candidateModel card, evals, known failures
CheckLaunch criteriaThresholds, blockers, export terms
OutputLaunch fileShip, hold, or needs more work
NextRegression checksFailures carried forward
Models: Model launch file
Model launches get messy when scores, failures, owner notes, and export terms live in separate tools. App Data gives the launch owner one clean release file instead of another dashboard to interpret.
Customer problem
The customer problem.
Model launches get messy when scores, failures, owner notes, and export terms live in separate tools. App Data gives the launch owner one clean release file instead of another dashboard to interpret.
App
Model Launch Check
Buyer
AI platform, MLOps, and model oversight teams
Send
model release candidate
Get back
Model launch file with eval summary, blocker list, regression cases, and scoped handoff files.
Status
Available now
Run
What AuraOne does
- Links scores, thresholds, and known failures to one candidate
- Flags below-threshold scores and unresolved blockers
- Separates launch blockers from general notes
- Prepares the scoped handoff once required items are present
Receive
What you get back
- Model launch file
- Blocker list
- Regression cases for the next candidate
- Scoped files for the team receiving the model
Steps
How it works.
01
Load the candidate
Bring the model card, evals, known failures, and export terms together.
- Score and threshold stay paired.
- Known failures are explicit.
- Owner notes stay attached.
02
Check the launch rules
The app compares the candidate against the release standard you provide.
- Threshold misses are visible.
- Regression blockers are separated.
- Missing terms stop the handoff.
03
Prepare the file
The system builds the release file only when the required parts are present.
- The receiving team sees the current status.
- Unresolved blockers remain named.
- Files are scoped to the release.
04
Carry forward misses
Failures become checks for the next release candidate.
- Repeated failures are easier to catch.
- The next candidate starts with known risks.
- The launch owner keeps the trail.
What this is good for
- Model release meetings
- MLOps launch readiness
- Model oversight prep
- Partner or customer handoffs
What this is not
- Replacing your training pipeline
- Claiming a model is safe for every use
Current readiness
Available now for one scoped release candidate. It is not a full MLOps platform replacement.
Start this app