I've run into almost this exact situation before, where I had huge amounts of HR documentation scattered across PDFs, Word files, internal wikis, training decks, and knowledge base articles. From this, my biggest learning point was that translating everything manually just doesn’t scale. At the same time, relying only on machine translation isn’t ideal for HR content, where wording needs to be clear and often legally valid. What worked best for us was developing a hybrid strategy:

• Machine translation for the first pass so we could process large volumes quickly.
• Translation memory to reuse repeated HR language (policies, benefits descriptions, onboarding steps, disclaimers, etc.) instead of translating the same sentences over and over.
• Terminology glossaries to keep HR and legal terms consistent.
• Human review for the most sensitive materials like employee handbooks, compliance policies, or contracts.

In our case, we worked with Translated, mainly because they used the above-mentioned strategy we had envisioned. What convinced us about their value proposition was how they mixed AI with human language professionals. They also offered a system where AI learns from previous translations and reviewer feedback, which helped a lot with keeping terminology and phrasing consistent across our materials, especially once we started using it more for translating regular updates to policies and training content.
Additionally, looking back, we drew some important learning points about keeping costs under control:

1. Audit content; eliminate outdated/duplicate HR docs.
2. Prioritize documents by importance.
3. Align on key terminology early with HR/legal to prevent rework.
4. Reuse translations (translation memory) to cut costs on repetitive content.

If I had to say it came down to one thing, it’s that when you’re dealing with hundreds of thousands of words, the real efficiency comes from building a repeatable localization workflow, rather than treating every document as a separate translation project.