Use case
Use case3 min readBy The Data Workers Team

Predictive maintenance data, run by agents

Sensor, CMMS and work-order data live apart, and models degrade when sensors are replaced or firmware changes. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Sensor, CMMS and work-order data live apart, and models degrade when sensors are replaced or firmware changes.
  • •What changes. Agents keep sensor streams mapped, join them to work orders and watch model drift.
  • •What stays with your people. Maintenance scheduling changes: they take lines down.
  • •The result. Failure predictions stay accurate as equipment changes.

The problem today

Sensor, CMMS and work-order data live apart, and models degrade when sensors are replaced or firmware changes.

What changes with Data Workers

Agents keep sensor streams mapped, join them to work orders and watch model drift.

The workflow: Real-Time Streaming agent (register_stream_schema) → Schema Evolution agent (detect_schema_change) → Pipeline Building agent (generate_pipeline) → MLOps & Models agent (detect_model_drift).
Fig. 1:The workflow: Real-Time Streaming agent (register_stream_schema) → Schema Evolution agent (detect_schema_change) → Pipeline Building agent (generate_pipeline) → MLOps & Models agent (detect_model_drift).

The workflow, step by step

  1. 1.The Real-Time Streaming agent calls register_stream_schema and keeps sensor streams in step.
  2. 2.The Schema Evolution agent calls detect_schema_change and maps sensor and firmware changes.
  3. 3.The Pipeline Building agent calls generate_pipeline and joins sensors to work orders.
  4. 4.The MLOps & Models agent calls detect_model_drift and watches failure models.

The impact metrics we move

  • •Sensor change mapped: from about a week to ~1 h
  • •Drift detected: from about a month to ~1 h
  • •Failure feature refresh: from about a day to under 1 h
Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.
Fig. 2:Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.

What goes on autopilot

  • •Sensor changes are mapped on arrival, so models never read broken signals.
  • •Model drift is watched, so predictions stay trustworthy.

Where your people stay in charge

Maintenance scheduling changes always go to a named approver, because they take lines down. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. One line's sensors and work orders.
  • •First week. Agents run at L1, observing only, and show the fixes they would have made, with receipts.
  • •By week two. The routine work above runs on its own; anything sensitive stays with a named approver.
  • •At day 30. We review the impact metrics against your baseline:
  • •Sensor change mapped: from about a week to ~1 h
  • •Drift detected: from about a month to ~1 h
  • •Failure feature refresh: from about a day to under 1 h
  • •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.

Related: The manufacturing hub · The full team playbook

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