Multi-Language Semantic Search

The Challenge

The client needed to search across over one million documents in two languages with semantic understanding — not just keyword matching. Existing search was slow, returned irrelevant results, and couldn't handle multilingual queries.

Our Approach

We built a vector-based semantic search pipeline using multilingual embedding models, optimized indexing strategies, and a hybrid retrieval approach combining dense vector search with sparse keyword matching for maximum recall. The system auto-detects query language and searches across both corpora.

Results

• 1M+ documents indexed and searchable
• <100ms p99 search latency
• 2-language support with auto-detection
• 85% improvement in search relevance vs keyword baseline

Tech Stack