Enterprise AI
A metropolitan transit network
A voice line that answers seven in ten calls itself
- Government & Public Sector
- Client under NDA
- Global
- 70%
- Calls resolved without a person
- 80%
- Faster on the top three call types
- 11
- Languages
Call volume ran far past what the support team could handle, and nobody could see in real time what callers wanted.
What we did
The system triages each call in the caller's language and hands it to a person when its confidence drops below policy. Pass extensions, fare questions and refund status make up most of the volume, and policy rules resolve them. Operations staff watch volume, call reasons and escalations live by location and time of day. Every call is logged for audit inside the network's core systems.
Related work
See all- A major financial institution
Financial Services/Client under NDA
A major financial institution saw 25% fewer fraudulent transactions after we put machine-learning scoring inside its banking systems.
25%
Fewer fraudulent transactions, caught in real time
- A manufacturing group
Manufacturing/Client under NDA
A manufacturing group lowered supply chain costs 20% with demand and inventory forecasts that feed its ERP.
20%
Off supply chain costs, with shorter lead times
- A hospital network
Healthcare/Client under NDA
A hospital network cut readmissions 15% with a risk score that appears in the clinical record before discharge.
15%
Fewer readmissions, with the prediction inside the EHR
Tell us which system the AI has to run inside
If that system is not ready for AI yet, tell us that too. An engineer will reply and say plainly whether we are the right firm for it.