# How AI Rebuilt Salesforce’s Decades-Old Localization Pipeline

[Salesforce](https://yomu.fyi/company/salesforce) · Scott Nyberg · Jul 23, 2026

**Type:** Problem & solution

## Summary

Salesforce needed to deliver major releases across 34 languages as product localization volume surged by more than 35% under fixed budgets and unchanged delivery windows. To address these scaling constraints, the engineering team replaced their legacy translation workflows with an LLM-powered orchestration pipeline integrating prompt engineering, context engineering, and multi-stage validation. The architecture transforms existing style guides, glossaries, and terminology databases into structured context, routing interface strings through specialized prompt stages—up to 85 stages for some languages—to enforce product context, grammar, and branding rules. Combined with an automated validation loop spanning AI editing, AI validation, and human review, this system reduced localization costs by 50 to 90% while accelerating turnaround times across enterprise releases.

## Context

Salesforce releases ship in up to 34 languages with the same functionality, quality, and schedule as the English release. Product-related localization volume surged by over 35% between releases, while translation budgets and release windows remained fixed, rendering human-only translation and basic machine translation insufficient for scaling.

## Approach / What changed

The engineering team built an LLM orchestration pipeline combining prompt engineering, context engineering, and multi-stage validation. Legacy style guides, glossaries, and terminology databases were converted into structured context for models. Strings move through sequenced prompts establishing grammar, branding, and product context, followed by AI editing, AI validation, and human review.

## Takeaways

- Depending on the target language, a single UI translation can move through as many as 85 specialized prompt stages before reaching production.
- The LLM-powered localization pipeline reduced localization costs by 50 to 90%, depending on the specific workflow, while accelerating turnaround times.
- Legacy style guides, glossaries, and terminology databases were restructured as contextual data used by models to generate, edit, and validate translations.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Scalability](https://yomu.fyi/topic/scalability)

[Read original post](https://engineering.salesforce.com/how-ai-rebuilt-salesforces-decades-old-localization-pipeline)