Managed Run

The input mix shifts, a supplier changes an invoice format, a model version is retired, the team stops trusting the drafts. Someone has to watch the numbers, fix what drifts, and report on it. Managed Run is that someone.

• A workflow is live and nobody looks at the numbers

• The team complains about quality and nobody can trace the cause

• Model, price-list or process changes break the agent without anyone noticing

• Weekly performance and quality monitoring

• Issue triage with defined response times

• Refinement backlog and releases

• Adoption support for the team

• Monthly KPI report and review

• Performance you can see, monthly, in numbers

• Drift caught before the team notices it

• A backlog that improves the workflow, not a ticket queue that slows it down

• Monthly report: volumes, automation rate, release rate, corrections, cost per case

• Issue log with cause, fix and time to fix

• Refinement backlog agreed with the workflow owner and shipped in releases

• Quarterly review with the sponsor: KPIs against the business case, next candidates

• Model, prompt and integration changes tested and documented before release

• Use case

  Document processing

  Invoices, order confirmations and certificates read, checked against order and master data, posted. Exceptions to a person.

• Insight

  Who owns the agent? An operating model for AI in operations

  An agent in production needs six decisions made and written down: who owns the output, who may change it, who watches it, who decides when it is unsure, who signs off risk, and who pays.

• Tool

  AI Act check

  First classification of a workflow under the AI Act, with obligations and dates.

• Method

  How we work

  Five principles, the first 90 days, and what we don't do.

• Next step

  Strategy call

  30 minutes. You bring the workflow, we tell you what it takes.