Extract Intelligence, Not Just Data

Multilingual Named Entity & Concept Extraction

Our hybrid linguistic engine leverages symbolic and statistical techniques to identify and normalize entities, terminology, and domain-specific concepts in multiple languages. It supports customized ontologies and taxonomies for granular tagging and cross-language alignment.

Semantic Relationship Extraction

More than co-occurrence: we extract typed relationships—causality, affiliation, ownership, roles—across sentences and documents. These outputs directly feed AI workflows like Graph-RAG, semantic search, or intelligent routing in LLM pipelines.

Software-first, Research-grade

The Bitext SDK is developed in C to maximize performance, scalability, and portability. Extremely lightweight at under 2MB, it can process over 60 million tokens per second per core, making it ideal for real-time deployment in high-volume environments. The SDK integrates seamlessly via REST API or Python bindings and supports both on-prem and cloud installations.

Built to Plug into the Graph-First AI Ecosystem

Bitext’s outputs integrate seamlessly with leading semantic and graph databases. Native compatibility with Neo4j, GraphDB, TigerGraph, RDF triple stores, Amazon Neptune, and Ontotext platforms ensures immediate deployment without transformation overhead.

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Working with 3 of the Top 5 Largest Companies in NASDAQ

AI-Ready Knowledge from Language

Finance: Enrich transaction records with named relationships like beneficiary, institution, legal role for fraud detection and KYC.
E-commerce: Build multilingual product graphs with brand, feature, usage, sentiment, and variant connections.
Security & Intelligence: Identify cross-language actor patterns, threat vectors, and geopolitical links from OSINT streams.
Compliance & Legal: Model roles, obligations, and ownership chains from multilingual regulatory texts.
Healthcare: Extract patient journeys, conditions, and treatment relationships across clinical records and guidelines.

Why Bitext

Bitext is built by computational linguists with decades of expertise in symbolic AI and multilingual NLP. Unlike generic data providers, we offer a software product—installable, private, and cloud-agnostic. Our technology is designed to be symbolic by design and multilingual by default, with proven performance in production environments across tech, telecom, and public sector domains.

NLG Technology to Generate Hybrid Datasets for LLM Fine-tuning

From Natural Language to Structured Knowledge

Ingest: Accepts plain text, HTML, PDF, or JSON in multiple languages with optional metadata.
Analyze: Linguistic models segment, tag, and normalize concepts and relationships using syntax, morphology, and contextual rules.
Export: Results are output in JSON-LD, RDF, CSV, or domain-specific schemas compatible with Neo4j, GraphDB, Amazon Neptune, Ontotext, and more.

This pipeline is ideal for powering semantic layers, Retrieval-Augmented Generation (RAG), and knowledge-based QA systems. Structured knowledge improves recall, grounding, and context management in LLM-based applications.

How LLM Verticalization Reduces Time and Cost in GenAI-Based Solutions

Verticalizing AI21’s Jamba 1.5 with Bitext Synthetic Text

Efficiency and Benefits of Verticalizing LLMs – The Case of Jamba 1.5 Mini.

Deploying Successful GenAI-based Chatbots with less Data and more Peace of Mind.

Customizing Large Language Models in 2 steps via fine-tuning is a very efficient way to reduce data needs, as well as training and evaluation efforts, when building customized Conversational Assistants. Bitext provides these Pre-Built Datasets and Models in 20 verticals.

Any Solutions to the Endless Data Needs of GenAI?

Discover the advantages of using symbolic approaches over traditional data generation techniques in GenAI. Learn how 100% reliable, bias-free, and PII-free data can be achieved through rule-based generation, ensuring semantic integrity and accuracy. Explore the unique benefits of this method for generating variations from seed sentences with predictable outcomes.

From General-Purpose LLMs to Verticalized Enterprise Models

In the blog “General Purpose Models vs. Verticalized Enterprise GenAI,” the focus is on the advantages of verticalizing AI models for specific enterprise domains. Verticalized models can disambiguate context-specific terms and speak in industry-specific tones. There are two approaches: building models from scratch, which is costly, or fine-tuning general-purpose models with domain-specific data. Bitext proposes a faster two-step method: first, verticalize the model, then customize it with enterprise data. This approach saves time, resources, and avoids common AI issues like hallucinations and bias.

Case Study: Finequities & Bitext Copilot – Redefining the New User Journey in Social Finance

Bitext introduced the Copilot, a natural language interface that replaces static forms with a conversational, proactive, and highly personalized user experience. This change not only simplified the onboarding process but also made it more interactive and capable of resolving queries in real time, offering significant advantages over traditional methods.

Abstract minimalist design visualizing Automating Online Sales with GenAI Copilots by Bitext.

Automating Online Sales with Proactive Copilots

Automating Online Sales with a New Breed of Copilots. The next generation of GenAI Copilots moves from passively answering customer questions to actively executing online sales. These new Copilots are proactive, they can start and drive an interaction with a potential customer; and context-aware, they know the different steps in the sales process, where they are in the process and how to move to the next step.

Taming the GPT Beast for Customer Service

GPT and other generative models tend to provide disparate answers for the same question. Having control is called Fine-tuning.

Can You Use GPT for CX Purposes? Yes, You Can

ChatGPT has major flaws that prevent it from becoming a useful tool in industries like Customer Experience

Need More Info?

At Bitext, we focus on linguistic-based language automation to deliver innovative customer experiences. If you want to test our solutions or learn more, we recommend you schedule a personalized demo from one of our experts.

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