By leveraging proprietary and specialized data within organizations, S2W builds a unique AI data ecosystem tailored to each domain, creating new business value.

At the core of domain-specific AI lies the ability to define the data structure of a specific domain—whether industry or organization—and to systematically model the relationships between data points. This process enables the construction of a Knowledge Graph, which allows for deep and precise analysis of data interconnections.

The resulting domain-customized data operation system goes beyond simple search functions. It evaluates causal relationships and prioritizes data based on significance, providing advanced intelligence that directly supports strategic decision-making.

S2W’s domain-specific AI is powered by three core components: Domain-specialized language models, Knowledge Graphs, and Generative AI.

S2W develops domain-specialized Large Language Models (LLMs) with exceptional capabilities in processing and understanding data within specific industries and domains. Our LLMs are designed to reflect each organization’s unique data and operational environment, delivering highly customized and optimized data operation solutions.

The performance of an LLM is heavily influenced by the quality and quantity of its training data. Therefore, effective data analysis, preprocessing, and refinement are essential. Furthermore, each dataset varies in source, nature, and quality, requiring a flexible and adaptive approach to identify and collect the most relevant data for the target domain.

Building specialized LLMs for areas such as cybersecurity, healthcare, and finance often goes beyond the capabilities of publicly available internet data. S2W overcomes this limitation by leveraging domain-specific sources such as expert publications, research papers, technical reports, source code, and even data from dark web forums. We also incorporate proprietary internal organizational data into our training pipelines.

Our approach includes not only data collection but also a thorough data cleaning process—removing noise, filtering duplicates, and applying accurate labeling. In addition, we utilize data augmentation techniques to enrich underrepresented datasets. Through this combination of high-quality domain data acquisition and refinement, S2W builds LLMs that deliver high efficiency and high performance.

These graphs are grounded in domain-specialized Large Language Models (LLMs), which provide the semantic foundation for accurately understanding complex domain data.

Our Knowledge Graphs integrate both structured and unstructured data from across internal and external sources. By doing so, they automatically identify and represent relationships between disparate data elements, enabling a unified understanding of organizational and industry-specific contexts.

With this foundation, S2W enables refined semantic analysis and automated data operations that reflect the nuances of each domain. These intelligently designed graphs break down traditional data silos, create consistent and connected data environments, and support advanced analytics and automation.

DarkBERT is the world's first Dark Web-specialized AI language model. A language model is an AI model that understands human language and has extensive pre-trained knowledge, making it highly capable of solving various natural language processing tasks. Among them, DarkBERT particularly excels in processing and analyzing unstructured data present on the Dark Web. Unlike other similar encoder language models that struggle with the diverse vocabulary and structural diversity found on the Dark Web, DarkBERT has been trained specifically to understand the illicit content present on the Dark Web. Furthermore, DarkBERT enhances its training by fine-tuning RoBERTa models through Masked Language Modeling (MLM) on text collected from the Dark Web.

The collection of the corpus is a fundamental challenge in training DarkBERT. S2W is renowned for its ability to collect and analyze data from the Dark Web, including its doppelgangers. It has accumulated a substantial Dark Web text corpus suitable for training by removing duplicates and low-density pages, resulting in a massive corpus of 5.83GB even after refinement.

DarkBERT utilized existing large-scale language models and further underwent post-training by incorporating specific domain data. It excels in handling unstructured data typically found on the anonymous web, where extraction can be challenging, and it is adept at inferring context. Additionally, DarkBERT can be employed for detecting/classifying various criminal activities occurring on the anonymous web and extracting crucial threat information.