Managed AI services
Keep the workflow useful after launch.
Worktree remains involved as your AI workflow meets real requests, changing systems, missing context, and unexpected cases. We review operation, evaluate performance, document material changes, and improve the deployment against an agreed standard.
One deployment under management
Every review should leave the workflow clearer than we found it.
Evidence is only useful when it leads somewhere. Worktree connects what happened in production to an owner decision, a tested response, and a current account of the deployment.
WT
Illustrative managed deploymentCustomer intake workflow
Operating evidence current
Review timeline
Illustrative reviewOne exception, followed through.
- 01ObservedException
A new intake arrived without the policy field required to prepare account context.
- 02RoutedBoundary held
The workflow stopped before action and sent the missing decision to the process owner.
- 03ImprovedTest passed
A focused source check was added and tested against the accepted evaluation set.
- 04RecordedRecord current
The remaining attachment limitation and next action were added to the Deployment Record.
- 01
Observe the work
Review the evidence that matters: completed work, exceptions, approvals, failures, and team questions.
- 02
Make a focused decision
Compare what happened with the accepted standard and route ambiguity to the person who has authority.
- 03
Improve without losing control
Test the smallest useful change, record what moved, and keep known limits visible.
Production changes the work
Launch creates an operating responsibility.
A workflow that passed its test cases will still meet changed policies, missing information, new language, and exceptions no one predicted. Managed operation gives those signals somewhere to go.
Quality and exceptions
Compare live operation with the standard accepted before launch. Give failures, unusual cases, and missing context a path to an owner.
Focused improvements
Test changes against representative cases before they alter the live workflow. Keep the approval boundary intact while the system improves.
A current deployment record
Keep the workflow's role, controls, limitations, material changes, and next actions understandable to the people responsible for it.
The machinery behind the service
See how the managed workflow stays inspectable.
The product keeps the role, selected systems, authority, evaluations, material changes, and next actions visible. It supports the relationship; it is not a platform your team must operate alone.
Before the managed-deployment review
Common operating questions.
What are managed AI services?
Worktree's managed AI services cover the ongoing operation around one defined workflow: reviewing relevant evidence, evaluating performance, following up on failures and exceptions, supporting the team within the agreed model, testing focused changes, and maintaining the Deployment Record.
Is this the same as MLOps or managed cloud infrastructure?
No. Worktree manages the business workflow and its operating responsibilities. Generic hosting and cloud administration are not the service being described here.
Does Worktree monitor every run around the clock?
No. The deployment establishes relevant operating evidence, a review process, and an agreed support model. Worktree does not imply continuous human observation or 24/7 service.
Who approves business decisions?
The customer retains final business authority. Worktree configures the agreed approval and escalation paths so consequential or ambiguous actions reach the appropriate person.
Give the operating responsibility a home
Bring us the workflow that needs an owner after launch.
Share what the workflow does, who owns its result, how quality is judged, which systems can change, and what happens when something fails today.