Search has been re-architected end-to-end. Instead of translating non-English content into an English “pivot” at ingestion and ranking everything in English, Native Multilingual Search ranks documents natively in their original language using next-generation retrieval models. This eliminates the quality loss of double translation and unlocks file search across 30+ languages.
The cross-lingual information retrieval (CLIR) / English-pivot approach we previously used is no longer how multilingual search works. Native Multilingual Search ranks documents in their original language using next-generation retrieval models.
In the Enterprise Search web app, you can type your query in a non-English language and get back relevant results if they exist. There are no settings to configure — language detection happens per query, so you can switch languages between searches at any time.
When you type your query, Search detects it automatically. Relevant results matching the query language will appear at the top, and as of May 2026, non-English search works across files as well as KBs and FAQs. When no strong in-language match exists, the ranker may surface cross-lingual results to ensure all relevant results across all your content are retrieved.
For 38 Tier 1 languages, Search combines both keyword retrieval and embedding retrieval (powered by Google’s Gemma embedding model) for strong relevance and cross-lingual matching, up to a global 5M of ingested documents across all connectors per customer. This gives customers broad language coverage with high-quality matching.
As customers scale beyond 5M documents, Search remains multilingual by leaning on keyword retrieval for those same 38 languages. This path is designed to keep latency predictable at very large scale, with the known tradeoff that some advanced language features (like synonyms and decompounding) may be more limited.
For Tier 2 languages beyond these 38, Search will rely on embedding-based and partial keyword retrieval for up to a global 5M of ingested documents across all connectors per customer. That means even outside the 38-key-language set, users can continue to search and discover relevant content across languages. At this time, we do not support multi-language search for Tier 2 languages beyond 5M documents.
Importantly, native multilingual search improves scale and cost efficiency. Because we no longer need to translate content at the time of ingestion and query understanding, ingestion can be faster and we can support larger corpora. Additionally, it improves precision for navigation and exact keyword matching use-cases compared to before, since now search occurs in the original language of the content.
In Native Multilingual Search, the search ranker treats geography and language as first-class signals rather than post-hoc filters. Admins no longer configure Hard / Soft / Disabled boost — the ranker handles locale-appropriate boosting automatically:
