Enterprise AI
A national telecom operator
Network outages fell 40% once the models could see them coming
- Telecommunications
- Client under NDA
- Global
- 40%
- Fewer network outages
- 50%
- Faster response to customers
- 20%
- Off operating costs
- $1M
- Saved each year through automation
The operator held years of network and customer data and had nobody in-house to turn it into decisions. Outages were diagnosed after the fact, and support response times kept rising.
What we did
An assessment of the data and infrastructure set the order of the work. Predictive models for the network came first, followed by assistants and sentiment analysis in customer service. Each phase came with training for the people adopting it.
Related work
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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 metropolitan transit network
Government & Public Sector/Client under NDA
A metropolitan transit network's voice line now resolves 70% of calls without a person, in eleven languages.
70%
Calls resolved without a person
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.