Use case
Use case3 min readBy The Data Workers Team

Churn and renewal risk data, run by agents

Usage, support and billing signals live apart, so risk is found at renewal. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Usage, support and billing signals live apart, so risk is found at renewal.
  • •What changes. Agents join usage, support and billing per account, keep risk features fresh and watch model drift.
  • •What stays with your people. Customer-facing actions: they affect relationships.
  • •The result. Risk surfaces months before renewal.

The problem today

Usage, support and billing signals live apart, so risk is found at renewal.

What changes with Data Workers

Agents join usage, support and billing per account, keep risk features fresh and watch model drift.

The workflow: Connectors agent (get_connector_status) → Pipeline Building agent (generate_pipeline) → Quality Monitoring agent (set_sla) → MLOps & Models agent (detect_model_drift).
Fig. 1:The workflow: Connectors agent (get_connector_status) → Pipeline Building agent (generate_pipeline) → Quality Monitoring agent (set_sla) → MLOps & Models agent (detect_model_drift).

The workflow, step by step

  1. 1.The Connectors agent calls get_connector_status and lands usage and support data.
  2. 2.The Pipeline Building agent calls generate_pipeline and builds risk features.
  3. 3.The Quality Monitoring agent calls set_sla and keeps them fresh.
  4. 4.The MLOps & Models agent calls detect_model_drift and watches the model.

The impact metrics we move

  • •Risk feature refresh: from about a week to ~1 h
  • •Signal gap caught: from about 3 days to 15-20 min
  • •Drift detected: from about a month to ~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

  • •Risk features stay fresh, so signals are current.
  • •Drift is watched, so scores stay trustworthy.

Where your people stay in charge

Customer-facing actions always go to a named approver, because they affect relationships. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Risk features for one segment.
  • •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:
  • •Risk feature refresh: from about a week to ~1 h
  • •Signal gap caught: from about 3 days to 15-20 min
  • •Drift detected: from about a month to ~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 sales and revenue page · The full team playbook

Related Posts

Use case

Account intelligence data, run by agents

Account data is split across CRM, billing, product usage and enrichment vendors that identify accounts differently. Here is how Data Workers runs it instead.

Use case

Accruals and prepaids data, run by agents

Accrual schedules live in spreadsheets, and missed or stale accruals surface in review. Here is how Data Workers runs it instead.

Use case

Agent governance and audit, run by agents

AI agents act on data with no consistent approval, record or rollback. Here is how Data Workers runs it instead.

Use case

Agentic controllership, run by agents

Controllers spend the month chasing reconciliations, approvals and evidence across ERP, billing and spreadsheets. Here is how Data Workers runs it instead.