Use CasesAI applications
AI/ML-Powered Tooling
Put useful recommendations inside the tools your team uses.
Illustrative scenario
Bring recommendations into the workflow.
An example of a possible engagement. The workflow and measures do not represent achieved client results.
A service team manually classifies incoming requests and decides who should handle them. Information is spread across systems, and similar requests are handled differently.
The work in practice
From friction to a useful change.
Current friction
- Review every requestRead messages and attachments individually
- Assign by judgmentInconsistent categories and priorities
- Reconcile mistakesReassign work and chase missing context
Proposed improvement
- Bring signals togetherRequest details and relevant history
- Suggest a category & ownerAI/ML recommendations in the work queue
- Review & routeStaff confirm, correct, or escalate
People stay part of the process
Where judgment matters.
Team members review recommendations during the pilot and correct mistakes. The process owner agrees any later automation, keeps exceptions visible, and reviews performance over time. We compare the approach with simpler routing rules before selecting it.
Agree a baseline and success criteria before introducing the change.