# Scaling Localization with AI at Lyft

DevFeed: [Scaling Localization with AI at Lyft](<https://devfeed.tech/articles/scaling-localization-with-ai-at-lyft-1243.md>)

Original publisher: [Read original article](<https://eng.lyft.com/scaling-localization-with-ai-at-lyft-b04dca99e6ee?source=rss----25cd379abb8---4>)

Author: Stefan Zier

Published: 2026-02-19T17:28:41Z

Content type: article

Language: en

Sources: [Lyft Engineering - Medium](<https://devfeed.tech/sources/lyft-engineering-medium.md>)

Topics: [Localization (l10n)](<https://devfeed.tech/topics/localization.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Web](<https://devfeed.tech/topics/web.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [aws](<https://devfeed.tech/tags/aws.md>), [code](<https://devfeed.tech/tags/code.md>), [compliance](<https://devfeed.tech/tags/compliance.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lyft](<https://devfeed.tech/tags/lyft.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [web](<https://devfeed.tech/tags/web.md>)

## AI overview

Lyft re-architected its translation pipeline to combine AI with linguist oversight, context injection, evaluation, guardrails, and version-controlled prompts. The batch system reduces translation latency from days to minutes while supporting legal compliance and brand integrity across expanding markets.

## Source excerpt

Written by Stefan Zier For years, Lyft's localization infrastructure relied exclusively on human translation. While this model usually ensured excellent quality, it was bound by multi-day turnarounds and costs that scaled linearly with every new language. For the few languages Lyft initially supported (Spanish, Portuguese, and French), these limits were acceptable. However, Lyft's expansion goals quickly outpaced what traditional workflows could support. Lyft's recent Québec launch required compliance with Bill 96 (legislation mandating French-first user experiences) which demanded faster turnaround than multi-day cycles allowed. Simultaneously, the Lyft Urban Solutions ("LUS": Bikes & Scooters) division sought to expand into European markets, requiring six new languages. The business need had changed as we now needed to move faster without sacrificing quality. This post explores how we re-architected Lyft's Translation Pipeline to leverage AI alongside linguist oversight and ultimately unlock new market launches. We will walk through context injection, decoupling content generation from evaluation, implementing guardrails, and treating prompts as version-controlled production code. The new pipeline reduces translation latency from days to minutes while maintaining the fidelity required for legal compliance and brand integrity. Note: We will walk through our batch translation pipeline -- used for 99% of app and web content -- which targets a 30-minute SLA for 95% of translations. We also support real-time translation (e.g., ride chat) which uses a different architecture.* How Translations Reach Hundreds of Services Before diving into the LLM pipeline, it helps to understand how translations flow through Lyft's infrastructure. This 2020 post explains the internationalization architecture initially built to move beyond one language/currency/country. Since then, the platform has grown to serve 11 locales across 150+ services. At its core, the pipeline does two things in