About Us

Bitext brings a unique approach to the market of Natural Language by combining symbolic computational linguistics and statistical machine learning. Bitext works in more than 70 languages and 25 language variants. Bitext works for the largest software companies in the world, for 3 of the 5 Big Tech.

Product. Bitext provides linguistic knowledge to make Generative AI reliable. With that goal, Bitext has engineered the best performing and most accurate Multilingual NLP SDK in the market. The main competitive advantages of the Bitext NLP SDK are:

Speed. Processes 640.000 words per second on an 8-core CPU
Multiplatform. Runs on any OS/Architectures: Linux, MacOS, Windows; ARM, x64
Multi-API. Native C available via C, Python, and Java APIs
Ubiquitous. Deployable both on premises and in the cloud
Light footprint. 50 MB HD, 200MB memory with no external dependencies

The Bitext NLP engine covers the full text analysis pipeline, from language identification to full parsing. Some of the main functionalities for 70+ languages and 25 language variants, including 4 variants of Arabic:

Language Identification at sentence level
Lemmatization & Word Segmentation, including Chinese & Japanese
Decompounding & Agglutination for German, Korean, Swedish, Turkish…
POS Tagging, including Phrase Structure Tagging
Entity Extraction
Concept Extraction and more

The SDK combines symbolic morphosyntactic analysis with configurable rule pipelines and semantic disambiguation layers. This approach enables deterministic, explainable extraction that scales efficiently across large document volumes and multilingual corpora.

The Bitext SDK is based on the largest lexical, morphological and grammatical resources in the market. These resources more than 70 languages and 25 language variants and contain more than 500 million words tagged with linguistic attributes.

Use Cases. The main uses cases in the current Generative AI trend are:

Entity and Concept Extraction. Extremely fast and efficient multilingual data extraction so entities and concepts can be easily consumed by vector search, graph databases, or compliance workflows.

Semantic RAG & Semantic Search. By tagging text with linguistic knowledge (POS, lemma, entities, concepts…) the Bitext SDK provides grounding, context control, and precision, reducing noise, hallucinations, and downstream inference costs in LLM-based systems.

Graph RAG. The Bitext SDK generates data items (concepts and entities) to semi-automate the creation of Knowledge Graphs from unstructured texts.