DarkBert can be customized and tuned to meet the specific needs of users. It has the capability to process a vast array of both internal and external unstructured data, enabling it to effectively filter and refine only the desired information from extensive datasets according to user preferences.

Customer A (Industry: Construction)

[Pain point]
There is a wealth of diverse language data available on the web that is crucial for corporate decision-making. However, many companies face challenges in directly scraping and analyzing this data due to insufficient internal infrastructure, especially a lack of expertise in processing unstructured language data. Even when a company possesses language processing expertise, handling domain-specific data can be highly challenging, requiring specialized tuning techniques.
(Example: Creating a tuned DarkBERT model for the dark web)

[Challenge]
The need arose to classify specific data or extract insights for decision-making from the vast amount of unstructured language data generated internally within the company. However, this data is highly domain-specific, making it exceptionally difficult to process effectively with general-purpose technologies.

[Result of Adoption]
By utilizing domain-specific language models, users can significantly reduce the time spent on data refinement by automatically pre-selecting meaningful data when attempting to extract insights from large datasets. Furthermore, when extracting specific statistics from the data, using language models that have pre-refined the data can enhance the reliability of the extracted statistics. The classification and refinement of such domain-specific data play a crucial role in enabling companies to make effective decisions based on their data.

DarkBERT plays a crucial role in the adoption of Large Language Models (LLMs) like OpenAI's ChatGPT within the enterprise. Companies are increasingly seeking to leverage various datasets, both internally and externally, for conversational purposes, where LLMs like ChatGPT generate responses based on this data. To achieve this, "Retrieval-Augmented Generation" (RAG) technology, which focuses on answer generation through search, has gained significant attention. However, a challenge arises due to the sheer volume of data, strong domain-specific characteristics (including domain-specific terminology), and the presence of irrelevant data, leading to reduced search efficiency and accuracy.

DarkBERT, as a "domain-specific encoder model," can address these issues in two key aspects:

(1)Domain-Specific Data Refinement and Classification:
DarkBERT utilizes models tuned to match the characteristics of a company's data. This allows it to automatically classify essential data relevant to decision-making according to the specific data features of the enterprise. Consequently, it enhances search accuracy and improves the quality of LLM responses.

(2)Domain-Specific Embedding:
One critical element of RAG is performing meaning-based searches, which necessitates appropriately embedding documents. General language models often lack an adequate understanding of data with strong domain-specific characteristics, making it challenging to generate embeddings that reflect the correct meaning. Models like DarkBERT, which undergo domain-specific tuning, enable the creation of high-quality embeddings. This, in turn, significantly boosts search accuracy for user queries.

DarkCHAT is a specialized generative AI model installed within a Dark Web monitoring solution called XARVIS. XARVIS required an effective system to refine and present information that users seek within the Dark Web. DarkCHAT enables users to effectively obtain threat intelligence related to their areas of interest. Leveraging the collected data, DarkCHAT derives new intelligence and grants users access to desired data through single commands.

Unlike commercially available language models, which cannot directly access the Dark Web and rely on curated Dark Web news from surface web sources, DarkCHAT stands apart as a real-time generative AI specialized for the Dark Web. It provides vivid, up-to-the-minute Dark Web information based on data collected from the Dark Web.


* Generative Artificial Intelligence is a technology that generates new data based on given data or inputs. It falls under deep learning and is also referred to as generative models. Generative AI can create various types of data, including text, images, audio, video, and more.