From manual localization bottlenecks to real-time market readiness
How Wamo eliminated translation lag and launched features simultaneously across countries
August 26, 2026 | United States
Executive Summary
For Wamo, the pan-European electronic money institution, speed to market is a competitive advantage. With simultaneous multi-market launches, continuous product updates, and regulated content requirements across multiple languages, localisation needed to operate at the same pace as the product. Manual, fragmented workflows delayed releases across markets and introduced operational overhead.
Uber AI Solutions implemented a system-level translation pipeline that reduced manual work by up to 90% and enabled simultaneous multi-market launches.
Key Results
80-90%
Reduction in manual localization effort
Less than 1-2 days
Localization turnaround time reduced from 3–5 days to less than 1-2 days
From 1- 2 to 3-4
Expanded automated simultaneous handling of multiple locales
When Global Expansion Outpaced Localization Capabilities
Wamo's growth depends on entering new markets quickly — and speaking to customers in their own language. As a fast-scaling fintech operating across multiple markets, localisation needed to match the pace of the product, not slow it down.
New market launches stalled because translated content lagged behind. Rather than a smooth rollout, each new market meant delays, fragmentation, and more coordination overhead.
Localization went from a workflow problem to a constraint on scaling.
“
For a fintech operating across multiple markets, localisation isn't a translation problem, it's an infrastructure problem.
”
Yanki Onen,
Founder, Co-CEO – Wamo
The Hidden Operational Cost of Fragmented Translation Workflows
Localization breaks at scale when continuous product updates meet fragmented workflows. Content originates across systems, changes constantly, and requires precise, regulated translation. Without system-level coordination, even small updates create delays, inconsistencies, and risk.
“
We needed it [localisation] embedded into how we
build and ship, not bolted on at the end.
”
Yanki Onen,
Founder, Co-CEO – Wamo
Designing a Scalable Localization Architecture for Growth
Wamo recognized that localization would have to be embedded into its product workflow if the company was to meet its growth ambitions.
Uber AI Solutions introduced a system-level localization model designed for continuous product updates:
Automation at the system level—not task level
Data-driven decisioning for routing, quality, and cost
A hybrid architecture combining machine translation and human review
Continuous synchronization between content systems and translation workflows
Wamo gained speed and control at scale—without adding operational overhead.
Building a Continuous Localization Pipeline Across Systems
The Uber system for Wamo operated as a continuous localization layer:
- Content changes triggered translation automatically
- Routing logic assigned work based on quality, speed, and cost
- Machine translation and human review operated in a coordinated loop
- Outputs were reintegrated into production systems in real time
Unlike traditional localization models, speed increased without sacrificing control, quality, or consistency.
Measurable Impact
The results were immediate and structural.
Manual effort dropped by 80-90%
eliminating the coordination burden that had previously slowed delivery.
Localization time dropped to less than 1-2 days
from 3–5 days, allowing content to move swiftly from creation to deployment.
Fully-automated simultaneous handling expanded to 3-4 locales
from just 1-2.
When a new feature is shipped, it goes live everywhere at once
whereas before, feature releases were staggered across markets.
Cost reduced
through translation reuse and automated matching, reducing redundant work across languages.
“
Working with Uber AI Solutions gave us the
system-level approach that matches how we operate.
”
Yanki Onen,
Founder, Co-CEO – Wamo
Enabling Real-Time Global Product Deployment
Now that localization is built into how Wamo operates, the company can:
- Launch features simultaneously across multiple countries
- Expand into new markets without scaling operational overhead
- Maintain consistent product experience across languages
- Respond to content changes in real time
- Build trust through accurate, localized communication
By bringing in Uber AI Solutions as its partner, Wamo turned localization from a scaling constraint to a system that enabled real-time global deployment.
Learn how Uber AI Solutions can partner with your enterprise to advance your AI.
Testimonials reflect individual results. Individual results are not a guarantee of outcome for any given customer, and customer experience will vary.
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