Domain-Specific AI
Domain-Specific AI for Real-world Operations
AI Grounded in How Your Operations Actually Work
Beyond General-purpose LLMs
Based on experience in processing and analyzing specialized-domain data, domain-specific LLMs, and a causality-based knowledge structure, we provide domain-specific AI through which AI can understand an organization's language, data relationships, and even its actual business context.
Why General-purpose LLMs
Are Not Enough
Where General-Purpose AI Breaks Down
General AI Lacks
Domain Context
- General-purpose AI does not sufficiently understand the unique terminology, business rules, and operational context used in an organization and industry.
- Results that do not reflect an organization's context remain generic answers, making them difficult to use for actual work and professional decision-making.
Enterprise Data Is Complex
& Unstructured
- A company's key information—documents, reports, emails, logs, and more—is scattered across diverse unstructured data, making it difficult to grasp the relationships and context between data.
- Unstructured knowledge has limits in discovering and leveraging an organization's hidden relationships and insights.
AI Results
Are Difficult to Trust
- AI presents results, but if it cannot explain the grounds for those results and their business context, actual use is difficult.
- AI that lacks explainability and trust struggles to take hold in work processes and decision-making.
From Domain Knowledge
to Trusted AI
Trusted AI grounded in domain knowledge
Massive Domain Data Curation
- Collecting and refining diverse data sources such as the open web, closed channels, internal systems, and specialized documents to build high-quality domain data assets that AI can learn from
- Making integrated use of data scattered inside and outside the organization and minimizing unnecessary noise that degrades AI performance
- Securing a data foundation suited to the actual work environment
Knowledge Graph-driven Relationship Learning
- Connecting refined data through knowledge graphs to structure the relationships between data—people, organizations, events, assets, documents, risks, and more—enabling context-based analysis and reasoning beyond simple information retrieval
- Grasping the hidden connections and scope of impact among scattered data to derive faster, well-grounded insights
Domain Ontology
& Semantic Understanding
- Systematically defining domain-specific concepts, terminology, and causal relationships so that AI can accurately learn and understand the meaning of specialized knowledge and business context
- Reflecting an organization's unique business rules and decision-making criteria to improve accuracy on domain-specific questions and deliver AI results that can be trusted in frontline practice
Efficient & Secure AI Deployment
- Building lightweight AI models that protect sensitive data while remaining applicable to actual operating environments, with stable operation even in closed networks, internal networks, and on-premises environments
- Building AI that complies with security policies while reducing infrastructure costs and operational burden
Build Domain-specific AI
from Real-world Knowledge
Domain-Specific AI Grounded in Real Operations and Field Expertise
01
Domain Data Collection
- Collecting and refining industry-specific specialized data, unstructured information, and internal operational data to build a foundation for AI learning
02
Knowledge Extraction
- Analyzing domain terminology, entities, and relationship information to extract the hidden knowledge and context within scattered data
03
Domain Specific Learning
- Building and training a domain-specific LLM through fine-tuning that reflects specialized knowledge and actual work scenarios
04
Context-aware Understanding
- Advancing AI to understand the context of the actual operating environment by reflecting an organization's language, business rules, and domain relationships based on embedding, retrieval, and knowledge-based reasoning technologies
05
Operational Deployment
- Applying AI to work systems and operational processes to build real-world usage environments for analysis, response, search, and work support
06
Continuous Improvement
- Continuously improving AI's performance and accuracy by reflecting the data and user feedback accumulated during operation
Domain-specific Language Model
Knowledge-driven AI
DarkBERT
The World's First Dark Web Language Model
The dark web contains specialized language and context that general language models struggle to understand, such as criminal slang, encrypted expressions, and unstructured text. S2W developed DarkBERT, the world's first language model specialized for the dark web, securing the technical capability to understand the meaning and context of dark web language and analyze the relationships between threat intelligence. DarkBERT is applied and used in real criminal investigation and security intelligence environments.
CyBERTuned
Domain-specific LLM for Cyber Threat Intelligence
Cyber threat data consists of specialized knowledge and complex relationships—attack techniques, malware, vulnerabilities, threat groups, and more—making it difficult to grasp the full attack context with general-purpose AI alone. S2W developed CyBERTuned, a language model trained on security expertise and real threat scenarios. CyBERTuned analyzes the meaning, relationships, priorities, and response context of threat intelligence, and is applied and used in real threat intelligence environments.
DarkBERT
The World's First Dark Web Language Model
The dark web contains specialized language and context that general language models struggle to understand, such as criminal slang, encrypted expressions, and unstructured text. S2W developed DarkBERT, the world's first language model specialized for the dark web, securing the technical capability to understand the meaning and context of dark web language and analyze the relationships between threat intelligence. DarkBERT is applied and used in real criminal investigation and security intelligence environments.
CyBERTuned
Domain-specific LLM for Cyber Threat Intelligence
Cyber threat data consists of specialized knowledge and complex relationships—attack techniques, malware, vulnerabilities, threat groups, and more—making it difficult to grasp the full attack context with general-purpose AI alone. S2W developed CyBERTuned, a language model trained on security expertise and real threat scenarios. CyBERTuned analyzes the meaning, relationships, priorities, and response context of threat intelligence, and is applied and used in real threat intelligence environments.
1/2
Explore More
Products We Offer
What's New at S2W
See our latest press coverage
S2W Contributes to INTERPOL’s African Cyberthreat Assessment Report 2026
2026.08.12
"As agentic AI raises jailbreak risk, defend by priority"
2026.07.27
"North Korean hackers combed blogs to pick out coin investors, planted malware in a "North Korea missions" folder"
2026.07.24
“Cyber threats know no borders, but responses must differ by country”
2026.07.03
