Case Study: e2f achieves rapid 1.77M-word localization into 6 languages in 10 days with Lilt's auto-adaptive MT

First Large-Scale Application of Auto-Adaptive MT

e2f, a San Jose–based translation and localization provider, was tasked by a large travel portal to localize 1.77 million words into six languages within a two‑week window. To meet this aggressive deadline, e2f partnered with Lilt and used Lilt’s auto‑adaptive machine translation platform as the translation engine to augment a team of 100+ translators, editors and project managers.

e2f implemented Lilt’s API-driven workflow with pre- and post‑processing, automated quality checks, and real‑time human-in-the-loop learning so translators could accept or revise suggestions and feed corrections back to Lilt. The result: throughput far exceeded the industry average of 335 words/hour (peaks over 1,000 wph), the job was finished in 10 days (ahead of schedule), quality was comparable to standard human translation with low error rates, and Lilt‑enabled productivity gains kept the project within budget.

e2f

Michel Lopez

Chief Executive Officer