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Your data science team built a good model. Now it needs somewhere to run.
Velocity lets a utility data science team or a vendor package a model, deploy it on ThreeV infrastructure, and run it against real inspection data without managing GPUs. Feedback from human review flows back so the model owner can improve it, and the model stays theirs.
Five stages, one evidence trail
Your models, running where the evidence chain already runs.
- Ten lines of Python, then it runs
Self-service packaging through the SDK. No platform expertise required, and no ticket to us to get a model deployed.
- Fails before production, not in it
Every uploaded version gets a smoke test in a sandbox with sample input. You get a stack trace and the actual input and output, not a silent bad deployment.
- The feedback loop closes
When a human reviewer corrects your model in Vision, that correction comes back to you as active learning data. Most deployment platforms are one-way.
- Retrain without moving your data
Retraining can run on ThreeV infrastructure against the feedback batch, so the data does not have to be pulled back into your environment first.
- Full lineage on every result
Findings are attributed to your model and version, so nobody has to guess which model produced which finding six months later.
- Groundwork for sharing across utilities
Models can be marked shareable. Utilities face largely the same assets and the same failure modes, and the work is currently duplicated in isolation at every one of them.
- ~10Lines of Python to deploy
- ZeroGPU infrastructure to manage
- YoursModel, weights and retraining stay with you
The people who have already built something and need it in production.
- Utility data science teams
Their models in production against real inspection data
- Vendors building for utilities
A deployment path that does not require building a platform first
- ML leads
Version lineage, smoke tests and a feedback loop instead of a black box
Not a marketplace yet
Sharing across utilities is the direction. Today it is deployment and management through the SDK.
Not a training platform
You own training. We run inference and can run retraining on your behalf against feedback data.
We do not take your model
Weights stay yours. Feedback comes back to you; retraining is yours to trigger.