Data Escalation Tiers
How telemetry is progressively filtered and promoted from raw edge streams to curated foundation datasets along the pipeline.
Not all data travels the whole way
The machines produce far more raw telemetry than could ever be stored or learned from in full. Data escalation is the principle that data is progressively filtered and promoted as it moves up the pipeline: everything is seen at the edge, a fraction is archived, and a curated subset becomes training data. Escalation decides what earns the trip to the foundation.
The tiers
At the edge, raw high-rate signals are consumed for control and mostly discarded after acting. The data fabric promotes validated signals and derived features for storage. The archive holds full pulse histories. Curation elevates the most informative of those into versioned training sets. Each tier holds less data but more distilled value.
- Edge tier: full-rate raw, consumed and largely discarded
- Fabric tier: validated signals and engineered features
- Archive tier: complete stored pulse histories
- Curated tier: labeled, versioned training datasets
What earns escalation
Promotion is not uniform. Anomalous or off-normal segments, a disruption precursor in the breeder, a potential-barrier wobble in the burner, are escalated preferentially because they carry the most information for learning. Routine steady-state data is subsampled. Escalation is thus an information-density decision, keeping the rare and surprising while thinning the ordinary.
Escalation is governed by metadata and lineage from the L2 data fabric, so every promoted sample knows where it came from and under what calibration. This is what makes the curated tier trustworthy: it is not a random sample but a governed, traceable selection from the full history.
The tiered design is what makes petabyte-scale operation sustainable for both machines. The foundation never tries to learn from raw firehoses; it learns from a curated distillate, while the full history remains available in the archive for replay whenever a study needs to reach back below the curated tier.