Tailored Generative AI Platform, SAIP

SAIP

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.

Product Overview

SAIP Overview

SAIP Overview
Key Features
Use Cases

S2W AI Platform, SAIP

The S2W AI platform, SAIP, serves as the foundation for domain-specific large language models (LLMs). This tailored solution integrates advanced AI technologies, including domain-specialized LLMs, knowledge graphs, ontologies, Retrieval-Augmented Generation (RAG), Role-Based Access Control (RBAC), and security guardrails. SAIP consolidates all forms of organizational data and delivers fact-based insights and responses tailored to user queries, reflecting the unique characteristics of each industry and the organization’s specific objectives.

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

Key Features

Structured and Unstructured Data Analysis

Leverages both structured data (e.g., databases) and unstructured data such as documents, images, and logs to generate sophisticated insights.
Multimodal Retrieval-Augmented Generation (RAG)

Improves the accuracy and reliability of AI-generated responses by analyzing diverse data formats, including text, images, tables, and PDFs.
Integration of Internal and External Data

Consolidates internal organizational data with external sources such as social media and news feeds to deliver timely, actionable business insights.
Multi-LLM Integration & Text-to-SQL Automation

Connects multiple large language models (LLMs) to provide optimal analytical results and automates data search and analysis by converting natural language queries into SQL.
Domain-Specialized Language Models & Knowledge Graphs

Supports customized AI analytics with language models optimized for specific industries and enhances data classification and search performance through domain-specific ontologies and knowledge graphs.
Enhanced Security Architecture

Strengthens data and system security with Role-Based Access Control(RBAC) and automatically enforces predefined security policies through security guardrails.

Benefits of Implementing SAIP

Instant Information Retrieval

Empowers employees to instantly access relevant information by leveraging the extensive knowledge stored in LLMs, enhancing operational efficiency and productivity.
Task Automation & Strategic Insights

Automates repetitive tasks such as data entry, analysis, report generation, and email responses, allowing teams to focus on strategic initiatives.

Provides comprehensive reports and actionable insights to support informed decision-making and business planning.
Efficient Data Analysis

Streamlines the analysis of large datasets and extraction of intelligence, improving the overall efficiency of data processing and decision-making.

SAIP Use Cases

Use Case 1

Use Case 2

Use Case 3

Lotte Memebers - Trend Analysis AI Platform (Industry: Finance & Retail)

S2W, in collaboration with Lotte Innovate, has developed Segment Lab, an AI-powered trend analysis and forecasting platform for Lotte Members. The platform integrates consumption data from 43 million L.POINT members with external news data to deliver advanced business insights. Built on S2W’s industrial-grade generative AI platform, SAIP, Segment Lab leverages domain-specialized LLM technology, an ontology-based knowledge graph, and RAG (Retrieval-Augmented Generation) capabilities, setting a new standard in trend analysis solutions.

Through Segment Lab, Lotte Group affiliates can generate customized insight reports on customer behavior, product sales, competitor activities, and market dynamics. The platform also enhances chatbot accuracy, providing high-level business intelligence across various operational scenarios.

By combining vast amounts of internal data with external sources such as social media content and online news, Segment Lab automatically analyzes customer behavior and product sales trends. This enables Lotte Members to extract meaningful insights and apply them to product development, marketing strategies, and business planning.

Hyundai Steel – Internal Knowledge Platform (Industry: Steel & Manufacturing)

Established in 1953 as South Korea's first steel company and a leading player in the nation’s steel industry, Hyundai Steel has integrated SAIP into its internal knowledge platform, HIP (Hyundai-steel Intelligence Platform). This integration enhances operational efficiency and technological capabilities across the organization. HIP supports employees by providing access to knowledge systems, streamlining internal document searches, and assisting with tasks through a management support chatbot.

HIP is the first application of an AI platform powered by Large Language Models (LLMs) in the steelmaking and refining sector. Leveraging expertise in unstructured data processing, the platform incorporates a big data system tailored to the steel industry. It utilizes an ontology-based SAIP to deliver accurate, context-aware responses while ensuring data security. By integrating Retrieval-Augmented Generation (RAG) and a robust security framework, HIP safeguards against data breaches and internal threats, providing reliable and secure AI-driven knowledge services.

Investigative and Government Agencies – DarkBERT & DarkCHAT (Industry: Cybersecurity)

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.