S2W AI Intelligence Platform. A safe enterprise tailored AI platform based on RAG and sLLM technology. It centralizes all structured and unstructured internal data to generate the fact-based answers and data in response to user queries.

SAIP Overview
  • SAIP Overview
  • Key Features
  • Use Cases
S2W AI Platform, SAIP

The S2W AI Platform, SAIP, functions as a foundational model for industry-specific Large Language Models (LLMs). It is built upon a secure design tailored to meet the security requirements of customers. S2W effectively addresses issues related to the security and reliability of generative AI, such as data breaches and the use of information from dubious sources, by employing a variety of AI technologies including Retrieval Augmented Generation (RAG) and Role-Based Access Control (RBAC).

What is Enterprise LLM?

A Large Language Model (LLM) is a pre-trained artificial intelligence model that can perform a wide range of Natural Language Processing (NLP) tasks such as summarization, translation, prediction, and generation, based on extensive datasets. An Enterprise LLM is a specialized version of LLM designed for business environments, enabling tasks such as workflow automation, customer service enhancement, and deriving insights from data.

How SAIP works
SAIP Key Features
SAIP Key Features
  • Accessibility to Extensive Knowledge 
    Access not only to the data within the company through the Knowledge Base but also to the most recent information.

    Possesses a wide range of capabilities and expertise in preprocessing unstructured data.
  • Semantic Search Engine
    Our search engine goes beyond traditional keyword-matching methods, instead, it comprehensively grasps user intent and understands contextual meaning to provide search results based on semantic relevance.
  • Security Enhancement Model
    We implement an optimized Large Language Model (LLM) construction and support system by leveraging architecture and data security technologies tailored to the security standards of enterprises.

    Our specialized security model is designed to anticipate and mitigate challenges related to perceptual issues and security incidents.
SAIP's Differentiator
  • Immediate Information Retrieval
    Accessing the extensive knowledge stored in the Language Model (LLM), our system enables employees to instantly retrieve relevant information, thereby enhancing productivity.
  • Task Automation
    Our system automates repetitive tasks such as data input, report generation, or email responses, allowing manpower to be strategically deployed for more critical responsibilities.

    It provides information for strategic decision-making and generates comprehensive reports and summaries to enhance overall business planning.
  • Efficient Analysis
    Enhancing the efficiency of analyzing large datasets and extracting insights, our system streamlines data processing and decision-making processes, fostering a more effective operational environment.
SAIP Use Cases
Use Case 1
Use Case 2
Use Case 3
Hyundai Steel - Internal Knowledge Information Platform (Industry: Steel)

Hyundai Steel, established in 1953 as South Korea's first steel company and a key player in the nation's steel industry, will integrate SAIP into its internal knowledge information platform, HIP (Hyundai-steel Intelligence Platform), starting from May 2024. This integration aims to further enhance the company's operations and technological capabilities.

HIP is the first instance of utilizing an AI platform in the steelmaking and refining sector with LLM (Large Language Models).

Leveraging unstructured data processing expertise from S2W, a big data system tailored for the steel industry has been built. This system utilizes an ontology-based SAIP to provide accurate responses, combining a secure structure with Retrieval-Augmented Generation (RAG) to protect against data breaches and internal threats, ensuring both accuracy and safety.

HIP is a generative AI technology platform accessible to all Hyundai Steel employees. It supports the provision of knowledge information systems to its employees, improves efficiency in internal document searches, and supports employee tasks and enhances efficiency through a management support chatbot.

S2W – DarkBERT & DarkCHAT (Industry: Cyber Security)

The dark web is characterized by a vast amount of unstructured data related to various cybercrimes such as drugs, weapons, and hacking. S2W has successfully developed and operationalized a specialized language model, DarkBERT, to establish a real-time big data pipeline capable of collecting, classifying, and analyzing threat data within the dark web.

DarkBERT exhibits exceptional performance in processing and analyzing unstructured data within the dark web compared to other Large Language Models (LLMs). This enables the detection and classification of diverse cybercrime activities, extracting key threat information.
However, the process of searching for essential threat information and understanding the related context still requires a significant amount of time. DarkCHAT, an AI application developed based on DarkBERT, is designed to address this challenge in dark web content.

DarkCHAT, integrated into XARVIS, is a specialized generative AI application for dark web content, featuring a question-and-answer-based unified search function for threat information. It significantly enhances user convenience and product usability by providing users with relevant threat information more quickly and efficiently.

Company A – Centralization of Internal Data (Industry: Construction)

Within any organization, there exists a diverse and extensive set of internal data that undergoes continuous changes or generates new content based on departments, timelines, and other factors. These data types range from documents, images to PDF files, taking various forms. Managing and effectively utilizing the multitude of derived data poses a significant challenge.

S2W's SAIP has addressed this challenge by centralizing massive unstructured data, establishing an AI platform capable of fetching and utilizing data as needed. We have aggregated vast amounts of internal unstructured data, conducting sophisticated language processing and analysis. This has led to the design of SAIP, a customized generative AI tailored for enterprises, allowing internal staff to search and retrieve information instantly.

Advanced natural language processing and dataset design enable SAIP to fetch the most relevant answers from all related information stored in the database. Through SAIP, internal staff can immediately access the desired information, contributing to the enhancement of operational efficiency. Moreover, the automation of repetitive tasks, such as report generation, has allowed for the allocation of work hours to more critical tasks