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AX Solution

It is an AI platform that supports private and hybrid environments,
enabling enterprises to create new value from generative AI development to operation.

XGEN Platform
Polar(sLLM)

Developed with PLATEER's proprietary AI technology accumulated over many years, it supports the entire AI lifecycle from data management to model development and deployment, collaboration, and monitoring. Built on intuitive workflows and secure governance, it delivers efficiency, performance, and trust all at once.

Enterprise tailored
AI for performance
Agile response through scalable architecture
Easy AI utilization with reduced complexity
Establishing a reliable AI operation environment
Strategic cost∙performance management
Continuous quality enhancement

XGEN Key Features

XGEN standardizes and accelerates innovation in enterprise AI utilization, from development to operation and scaling.

Feature 01

For easy AI service
development AI Service Generator
Feature 02

Designed for scalability
and flexibility XGEN LLMOps
Feature 03

Tailored for vertical
domains XGEN LLM
Feature 04

From development to
operation One-stop service setup

XGEN Scalability

XGEN swiftly and flexibly integrates not only Private LLMs but also external Public LLMs such as ChatGPT and Gemini.

Private

XGEN Private — Components

User

XGEN

Employee

Vector DB / Embeddings

Private LLM

Data Flow

The user sends a request (Prompt, Query) to the XGEN system.

XGEN interacts with the Employee, Vector DB, and Private LLM through the internal network.

The Vector DB retrieves relevant information from internal data using embeddings.

The Private LLM generates a response based on the retrieved context.

The final response is delivered back to the user through XGEN.

Configuration of internal Private LLM (requiring GPU infrastructure and fine-tuning)
Minimizing data leakage to external networks

Hybrid

XGEN Hybrid — Components

User

XGEN

Employee

Vector DB / Embeddings

Private LLM

Public LLM

Data Flow

The user sends a request or query to XGEN.

XGEN interacts with both internal (Employee, Vector DB, Private LLM) and external (Public LLM) systems.

Internal data is retrieved from the Vector DB via embeddings to provide contextual information.

Public LLM contributes general language reasoning or knowledge-based support.

XGEN synthesizes responses from both sources and returns the final answer to the user.

Integration/unification with external LLM (e.g., GPT) APIs (cost incurred based on tokens)
Utilizing internal data through Retrieval-Augmented Generation (RAG)

XGEN Architecture

The XGEN platform is designed with an architecture that offers scalability and flexibility, allowing it to respond organically to diverse domain environments.

XGEN Platform의 전체 구조를 보여주는 다이어그램 — Domain

E-Commerce

Public institution

Service

Finance

Ect

Use Case AI Apps

Customers

AI Search

AI Chatbot

Improving commerce operational efficiency

Copywriter

Customer consultation analysis

Campaign product recommendations

Automated marketing image and video creation

Internal business system

Internal document creation/search

AI Code Assistant

XGEN Platform

AI Service Generator

Workflow Canvas

AI Chatbot

Copywriter

AI-API's

Deployment management

API integration

Embed

Webpage

RAG management

Embedding model

Vector DB

Re-ranker

Data Source

Structured data

ERP/SRM

CRM/MES

Policy/Regulation

Unstructured data

PDF, WORD, PPT

Email, VOC

Corporate characteristics/culture

LLMOps

Training data management

Pre-training

Fine tuning

Model evaluation

Model saving

Model service

Set Service Settings

Configure LLM-based service environment

Connect Vector DB

Ondemand GPU management

Ops monitoring

Foundation Model (LLMs)

Public LLM

ChatGPT

Gemini

EXAONE

Private LLM

XGEN LLM

Polar

Gemma

deepseek

Qwen

Infra

Cloud

AWS

Microsoft Azure

Google Cloud

On-Premise

In-house server

Storage

Network/Security

Polar

Polar supports companies and services developed by flashlightly protect corporate data, and services based on this, and services based on this.

XGEN LLM

Foundation Model (LLMs)

Public LLM
Private LLM

E-commerce specialized Polar key features

Optimizing Korean

Maximize practical work utilization and response quality through 480 million Korean and e-commerce data-based learning
Accurate and Reliable Response

Ensure reliable responses by reflecting the latest information through RAG-based real-time search and generation
Stable Operating Environment

Combine e-commerce operation expertise with sensitive data filtering and monitoring to ensure both stability and security
Efficient Integrated Management

Integrate UI, embeddings, and vector DB seamlessly to enhance performance and reduce operational costs

Polar(e-commerce specialized sLLM)

Polar(e-commerce specialized sLLM) — sLLM + RAG-Based Question Answering Flow

The user enters a Prompt or Query.

The system sends the Query to the Knowledge Content Repository (RAG).

The RAG searches related documents and returns an augmented context.

The system passes the Prompt + Query + augmented context to the sLLM.

The sLLM generates the final Text Response and returns it to the user.

Components

RAG (Retrieval-Augmented Generation)
- Retrieves relevant information from documents and databases to create contextual input.

sLLM (Specialized Large Language Model)

Analyzes user queries

Validates answers

Processes generation requests

Generates responses

→ Both pre-trained and fine-tuned models are used together.