AI

AX Partner: Bridging Business and Technology

  • AI

  • AI Development

AI Development

Turning strategy into action and ideas into systems
AI agent technology, which enables AI to autonomously assess problems and carry out the tasks required to solve them, is gaining attention as a next-generation AI solution.

We deliver a new class of AI agents tailored to our customers’ complex business processes.
From reactive AI to execution-oriented AI
AI Agent
Function

Autonomous actions to achieve objectives

Decision-making Approach

Integrates multiple data sources to execute optimal actions (Proactive)

Technical Components

Data retrieval, API integration, utilization of external tools

Examples

Hyper-personalized wealth management agent, credit card fraud investigation agent ···

AI workers that carry out real tasks

They do more than answer questions—they carry out real tasks.

AI Chatbot
Features

Answering user questions

Decision-Making Method

Responses based on predefined conversation flows (React)

Technical Components

Rule-based, predefined responses, RAG

Examples

General conversational services like ChatGPT, enterprise customer service chatbots ···

AI Information Assistant

Answers user questions and provides information

LLM (Large Language Model)
Function

Generates natural language by learning from vast amounts of data

Decision-Making Method

Generates the most appropriate answer based on probability

Technical Components

Transformer-based natural language processing

Examples

OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini ···

Brain

The core engine of AI that processes information and learns

Understanding the Workflow and Architecture of AI Agents

AI 에이전트의 흐름과 구성 이해하기

Key Benefits Provided by AI Agents

Detailed work plans/actions to achieve set goals
Minimal human intervention, independent judgment and action
Self-learning from work results, continuous performance improvement
Collaboration among AI agents with specialized roles, Human-in-the-Loop

Key Services

Category
Interactive Agent
Task Automation Agent
Multi-Agent System
Decision Support Agent
Definition
An agent that converses in natural language to understand and fulfill user requests
An agent that automatically performs repetitive and routine tasks
A system in which multiple specialized agents collaborate to handle complex tasks
An agent that analyzes complex decision-making processes and proposes optimal solutions
Key Features
  • Natural Language Understanding (NLU)
  • Contextual Awareness
  • User-Friendly Interface
  • Rule-Based Automation
  • 24/7 Unattended Operation
  • Minimized Error Rate
  • Role-Based Specialization
  • Inter-Agent Collaboration
  • Handling Complex Workflows
  • Data-Driven Analysis
  • Simulation of Various Scenarios
  • Providing Decision-Making Rationale
How It Works
  • 1. User enters a question
  • 2. Intent is identified and analyzed
  • 3. Required actions are executed
  • 4. A response is generated in natural language
  • 1. Detect trigger events
  • 2. Execute predefined rules
  • 3. Record and report results
  • 1. Analyze tasks and assign roles
  • 2. Execute tasks in parallel across agents
  • 3. Share interim results
  • 4. Make a consolidated decision
  • 1. Collect and analyze data
  • 2. Evaluate scenarios using AI models
  • 3. Derive the optimal decision
  • 4. Present the decision along with supporting rationale
Use Case
A customer starts the insurance claim process through a chatbot
Automatic document classification and OCR processing
  • Agent 1 : Policy analysis
  • Agent 2 : Claims calculation
  • Agent 3 : Anomaly verification
Final approval decision and justification
Key Benefits
  • Improving Customer Satisfaction
  • 24/7 Immediate response
  • Reducing the workload of counseling staff
  • Processing time reduced by 70–90%
  • Labor cost savings
  • Unattended processing during nights and holidays
  • End-to-end automation of complex tasks
  • End-to-End Processing
  • Significant improvement in processing speed
  • Improved Decision-Making Accuracy
  • Risk Prediction
  • Ensuring transparency in decision-making
Core Technology
LLM, RAG, NLP
RPA, Rule Engine, API Integration
AI Orchestration, Workflow Engine
ML models, predictive analytics, simulation