Enterprise AI
An online retailer
Recommendations that lifted an online retailer's sales 20%
- Retail
- Client under NDA
- Global
- 20%
- More sales, with higher engagement
Competition was fierce, and the retailer needed more from its existing traffic without spending more to acquire it.
What we did
Recommendation models learn from browsing and purchase history. Each customer segment gets its own offers and page layout, and every change is tested against conversion before it stays.
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 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.