Use cases
How teams put AI to work, with people in control
Example scenarios for support, retail, hospitality and internal operations. They are illustrative, not client results: named results appear only with a client's approval.
Example scenarios
Where AI fits into day-to-day work
Customer support
AI-assisted support triage
Route incoming questions, summarise customer context, draft replies and hand off sensitive cases to a human operator.
- Response backlog
- Repeat questions
- Escalation quality
Retail and ecommerce
Lead and order follow-up
Answer product and order questions across chat and messaging channels, keep the conversation history visible and hand follow-up to your team.
- Lead quality
- Follow-up time
- Conversion path
Hospitality
Guest workflow review
Audit where AI can answer, qualify, summarise or escalate without taking over guest-impacting decisions too early.
- Enquiry volume
- Human approval
- Service consistency
Operations
Internal workflow automation
Let AI carry repetitive internal requests through your systems, with integrations, an audit trail and a person approving what matters.
- Manual steps
- System handoffs
- Operator review
How we measure
Baseline first, then measure what AI changes
We baseline the workflow, deploy with human-control gates, then measure the operating signals that matter.
Workflow baseline
The current response path, repeated tasks, handoff points and owner decisions, documented before AI is added.
Measured operating signals
Response time, qualified leads, handoff rate and follow-up quality, tracked after launch.
Human control
Where the assistant escalates, asks for approval, or lets a person take over.
Verified customer evidence
Named results are published only with the client's approval, a source and a time period.