Where we operate Oceania·Asia·North America·Europe Built for teams operating across multiple markets — local delivery, time-zone overlap, regulator-aware drafting.
The cases
Three deployments. Three results. One operating model.
Click into a case to see what piled up, what the AI employee prepared, where approval stayed human, and what improved after week one.
[Energy · legal & regulatory drafting A tier-1 Australian energy retailer.
Documents that used to take days now ship in under an hour. The in-house legal and regulatory team reviews and revises instead of retyping.
Cycle time
Days → under 1 hour
Per first draft, end to end
Read the case→](/content/case-studies/energy-legal-drafting/index.html)
[Higher education · course advice & customisation A leading Australian provider of online higher education.
Deeper answers, customised pathways, better experience for every prospect. The advisors who used to type the same unit explanations now spend the day on the conversations that need them.
What moved
CSAT & prospect experience
The quality of every first reply
Read the case→](/content/case-studies/higher-ed-course-advice/index.html)
[Telecommunications · chat triage The national telecommunications carrier of a small island nation.
A thousand customer chats a day, moved through the day instead of building. Agents open each chat to a pre-built brief, not an empty draft.
Volume
~1,000 chats/day
Across the carrier’s primary channel
Read the case→](/content/case-studies/telco-chat-triage/index.html)
How a case stays useful
The weekly tune is the work.
A first deployment is the easy part. The reason these cases are still running is the cadence around them.
Weekly review
Every employee has a 30-minute review each week — what got rejected, what got handed back, what should it own next.
One or two new skills at a time
Not a list of twenty. Two specific moves, shipped and tuned the following week.
Approval gates stay tight where it matters
Sending, paying, committing — human-approved by default. The argument for opening a gate is made with evidence, not pitched up-front.
Handbacks are healthy
When the employee says “I’m not sure — routing to you”, that’s the system working. The goal isn’t 100% autonomous; the goal is the right things in the queue.
Where exact customer-side metrics aren’t disclosed, ranges shown on each case reflect bands typical of comparable AI-employee deployments published by vendors in the same category. The real deployments described operate within these bands.
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