Bitext + Elasticsearch

Multilingual NLP for better relevance, semantic search, and reliable GenAI.

Bitext brings a unique approach to Natural Language Processing by combining
symbolic computational linguistics and statistical machine learning.
Bitext supports 70+ languages and 25 language variants and works with the world’s largest software companies,
including 3 of the 5 Big Tech.

As an Elasticsearch partner, Bitext enables search and AI teams to improve relevance, consistency, and extraction quality across large multilingual content collections—especially in morphologically rich and compound-heavy languages.

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Seamless Integration with Elasticsearch

We integrate advanced word segmentation, lemmatization, and decompounding into Elasticsearch through custom language analyzers and token filters. This enables linguistically normalized indexing and querying, delivering higher precision and recall—without changing application logic or query patterns.

  • Higher relevance across multilingual collections
  • Improved recall for compound-heavy languages (e.g., Germanic languages)
  • Normalized indexing and querying with no application changes

Bitext Multilingual NLP SDK (Key Strengths)

Bitext provides linguistic knowledge to make Generative AI reliable, offering one of the best performing and most accurate multilingual NLP SDKs in the market.

  • Speed: 640,000 words/sec on an 8-core CPU
  • Multiplatform: Linux, macOS, Windows; ARM & x64
  • Multi-API: native C engine with C, Python, and Java APIs
  • Ubiquitous deployment: on-premises or cloud
  • Light footprint: 50MB disk, ~200MB RAM, no external dependencies

Core Capabilities

The Bitext NLP engine covers the full analysis pipeline—from language identification to advanced extraction for 70+ languages and 25 variants:

  • Sentence-level Language Identification
  • Lemmatization & Word Segmentation (including Chinese & Japanese)
  • Decompounding & Agglutination (German, Korean, Swedish, Turkish…)
  • POS Tagging, including Phrase Structure Tagging
  • Entity Extraction and Concept Extraction

This approach combines deterministic linguistic parsing with configurable rule pipelines and semantic disambiguation, enabling explainable, scalable extraction across large document volumes.

Use Cases

  • Semantic Search & Semantic RAG: more grounding and precision, less noise and hallucinations.
  • Entity & Concept Extraction: fast multilingual enrichment for vector search, graphs, and compliance.
  • Graph RAG: structured signals to accelerate Knowledge Graph creation from unstructured text.

Get Started

If you are building multilingual search, semantic retrieval, or RAG pipelines on Elasticsearch, Bitext can help you improve relevance, reduce noise, and accelerate structured enrichment at scale.

MADRID, SPAIN

Camino de las Huertas, 20, 28223 Pozuelo
Madrid, Spain

SAN FRANCISCO, USA

541 Jefferson Ave Ste 100, Redwood City
CA 94063, USA