Live-environment signal
Data is captured during normal industrial activity with real interruptions and object variability.
Telepath data programs run inside active operations, not staged capture days. That means your datasets include the irregularity, timing, and exception patterns models must handle outside of demos.
Data is captured during normal industrial activity with real interruptions and object variability.
Collection scope, QA thresholds, and acceptance criteria are set before execution to protect delivery consistency.
Deliveries are structured for downstream training and evaluation pipelines, reducing manual cleanup burden.
Programs prioritize scenario classes that correspond to real deployment failures rather than broad but shallow data volume.
Once baseline quality is stable, collection can extend to adjacent workflows while preserving schema and QA consistency.
Teams can refine collection objectives as model performance changes, focusing each cycle on the next capability bottleneck.
Yes. Telepath can scope collection to include the signals required by your training and evaluation stack, within agreed delivery standards.
Collection objectives, QA checks, and delivery criteria are defined upfront and applied consistently to each recurring drop.
Yes. Teams often begin with a single high-value workflow, validate signal quality, then expand scope in phases.