Select Language

Services

AI Training & Implementation AI Agent Development LLMO Optimization Content Creation SEO Services Social Media Management Shopify Development Website Development AI Dock (Technical Debt Diagnosis)Web Advertising MEO (Map Engine Optimization)Marketing Automation LPO (Landing Page Optimization)AI Business Consulting AI Governance & Security AI Managed Services AI In-house Enablement AI Data Analytics Platform CRM/SFA Implementation Whitepaper Production GA4/Web Analytics ABM Implementation AI Knowledge Search (RAG)AI Chatbot Development AI Meeting Minutes & Transcription Prompt Engineering Training DX Consulting AI × RPA Automation AI Fine-tuning Service AI Test Automation Service Inside Sales Setup Email Marketing B2B Branding & PR Sales Enablement Lead Generation & BDR AI Search Optimization B2B Video Marketing CDP Implementation B2B EC Development AI Knowledge Base (RAG)TikTok Management YouTube Management EC/D2C Consulting Recruitment Marketing Webinar Production LP Production & Optimization New Business Consulting Web Marketing System Development

About Us Case Studies Seminars Media Resources

Free Consultation

Service

AI Agent Development

AI agent development is a service that designs and builds AI systems using LLMs to autonomously perform business tasks.

Develop AI agents that automate business processes. Operating 24/7 to free human resources, achieving cost reduction and productivity gains.

Get a Free Consultation What Is an AI Agent

What is AI Agent

What Is an AI Agent

An AI agent is an AI system that autonomously executes tasks with a large language model (LLM) at its core. Unlike traditional chatbots, it can handle complex decisions, use external tools, and learn continuously.

Differences from Traditional Chatbots

Traditional Chatbot

-Responses based on predefined rules
-Keyword matching branching
-Difficulty handling unexpected questions
-Requires effort to add/modify scenarios

AI Agent

Natural dialogue with context understanding
Flexible handling of complex questions
Task execution integrated with external systems
Continuous learning and improvement

Key Capabilities of AI Agents

Reasoning Capability

Breaks down complex problems step by step and derives logical solutions. Capable of situational judgment, not just simple pattern matching.

Tool Use

Autonomously uses various tools such as external APIs, databases, and file systems to achieve objectives.

Memory & Learning

Remembers past conversations and processing results, maintaining context for consistent responses. Long-term improvement is also possible.

Types of AI Agents

Task-Specific

An agent optimized for specific tasks. Ideal for automating routine operations such as inquiry handling, data entry, and report generation.

Use Cases

FAQ Response BotData Entry AssistantSchedule Management

General-Purpose

An agent capable of handling a wide range of tasks. Understands user intent and selects the appropriate actions to execute.

Use Cases

Internal AssistantMulti-Task AgentInformation Search & Summarization

Autonomous

An agent that completes complex tasks with minimal instructions. Autonomously handles everything from planning to execution and result verification.

Use Cases

Research & Analysis AgentWorkflow AutomationProject Management

Use Cases

AI Agent Use Cases

AI agents are active in various business scenarios. Custom development tailored to your challenges is also available.

Customer Support Automation

Automate inquiry handling with an AI chatbot available 24/7. Reduce operator workload and improve customer satisfaction.

24/7 availabilitySignificant reduction in response timeReduced operator workloadConsistent response quality

Sales Support AI Agent

Streamline sales activities from lead acquisition to deal follow-up. Contribute to improved close rates with data-driven proposals.

Automated lead acquisitionDeal data analysisAutomated follow-upAuto-generated proposal materials

Data Analysis & Report Automation

Automatically collect and analyze scattered data to generate reports needed for decision-making. Improve the speed and accuracy of business decisions.

Automated data collectionReal-time analysisAuto-generated reportsAnomaly detection alerts

Internal Knowledge Search AI

Learn from internal documents and manuals to enable natural language search. Quickly access the information you need and boost operational efficiency.

Natural language searchInternal document learningImproved answer accuracyKnowledge accumulation

Implementation Types

Types of Implementation

Various types of AI agents can be deployed based on your business challenges.

Internal Inquiry Handling

Automatically responds to internal inquiries from HR, accounting, IT, and more. Reduce staff workload by training it on manuals and FAQs.

Automation of routine questions
24/7 availability
Consistent answer quality
Response history accumulation

Document Search & Summarization

Cross-search internal documents, manuals, and meeting minutes, then summarize and present the needed information. Dramatically reduce time spent searching for information.

Natural language search
Automatic extraction of related documents
Summary & organization features
Source citation display

Data Analysis Report Generation

Analyze sales data, customer data, access logs, and more to automatically generate periodic reports. Support data-driven decision-making.

Automated periodic reports
Trend analysis
Anomaly detection
Auto-generated charts & tables

Booking & Schedule Management

Automate meeting room bookings, appointment coordination, and reminder notifications. Integrate with calendars and email for efficient schedule management.

Automatic availability search
Automated participant coordination
Reminder notifications
Change & cancellation handling

Customer Support

Respond to customer inquiries 24/7. Automate product information, troubleshooting, order status checks, and more to improve customer satisfaction.

24/7 availability
Multilingual support
Escalation functionality
Response history analysis

Process

Development Process

Requirements Definition

We conduct detailed interviews of your business workflows to clarify the functions and performance required of your AI agent.

Design & Prototyping

We design the system and create an early prototype, incorporating feedback as development progresses.

Development & Testing

We carry out full-scale development and conduct quality assurance testing to confirm stable operation.

Deployment & Operations Support

We support deployment to your production environment and provide continuous improvement and maintenance after launch.

Technologies

Technologies Used

Combining the latest AI technologies to build the optimal agent.

OpenAI API

LLM

Claude API

LLM

LangChain

Framework

RAG

Architecture

Vector DB

Database

Python

Language

Tech Stack Details

Technology Stack Details

Combining the latest AI technologies to build the optimal agent.

LLM Providers

OpenAI GPT-4

A general-purpose model with advanced reasoning and generation capabilities

Anthropic Claude

A model excelling in long-text comprehension and safety

Google Gemini

A next-generation multimodal model

Azure OpenAI

A secure environment for enterprise use

Frameworks

LangChain

The standard framework for LLM application development

LlamaIndex

Specialized in data indexing and retrieval

Semantic Kernel

Microsoft's AI orchestration framework

AutoGen

Building multi-agent conversations

Vector Databases

Pinecone

A fully managed vector database

Weaviate

An open-source vector search engine

Qdrant

High-performance vector similarity search

pgvector

A PostgreSQL extension for vector databases

Deployment

Cloud (AWS/GCP/Azure)

Scalable and flexible operations

On-Premises

Keep sensitive data within your organization

Hybrid

A combination of cloud and on-premises

Private Cloud

High-security operations in a dedicated environment

Security & Operations

Building enterprise-level security measures and continuous operational improvement systems.

Data Privacy

Policy design for handling sensitive information. Implements data encryption, access restrictions, anonymization, and more.

Data encryption (in transit & at rest)
Personal information masking
Data retention period management
GDPR / Data protection compliance

Access Control

Information access control based on user permissions. Minimizes data leakage risk by providing only the necessary information.

Role-Based Access Control (RBAC)
SSO/SAML integration
API authentication & authorization
Audit log recording

Monitoring & Logging

Continuously monitors system uptime and AI response quality. Used for early problem detection and quality improvement.

Real-time uptime monitoring
Response quality monitoring
Error & anomaly detection
Usage statistics visualization

Model Updates & Maintenance

Handles LLM updates and additions/updates to training data. Enables continuous accuracy improvement and adoption of the latest technologies.

Regular model evaluation
Prompt optimization
Knowledge base updates
New feature additions

FAQ

QHow long does the development process take?

It depends on the complexity of the requirements. A simple chatbot typically takes 1-2 months, while systems with complex integrations may take 3-6 months. A prototype can be created in 2-4 weeks.

QCan you integrate with existing systems?

Yes, we support integration with existing systems through API connections, database integrations, and more. We have integration experience with major tools including Salesforce, kintone, Slack, and Microsoft Teams.

QIs there support after launch?

Yes, we offer operations and maintenance services. We provide continuous support including regular model updates, performance monitoring, incident response, and feature additions.

QWhat security measures are in place?

We implement enterprise-level security measures including encryption of sensitive data, access control, and audit log collection. We can also build systems in on-premises environments.

QWhat's the difference between an AI agent and a traditional chatbot?

Traditional chatbots are rule-based and limited to predefined responses, while AI agents leverage large language models (LLMs) for natural, context-aware conversations. They can also perform more advanced tasks such as integrating with external systems and automating task execution.

Glossary

AI Glossary

Explanations of technical terms commonly used in AI agent development.

RAG (Retrieval Augmented Generation)

RAG / Retrieval Augmented Generation

A technique that retrieves relevant information from external databases and uses it as the basis for LLM-generated responses. Commonly used in Q&A systems leveraging internal documents.

Prompt Engineering

A technique for optimizing inputs (prompts) to obtain desired outputs from LLMs. Improves response accuracy and consistency by providing appropriate instructions and examples.

Fine-Tuning

Additional training of a pre-trained model with data from a specific task or domain. Enhances the model's ability to handle specialized terminology and industry-specific expressions.

Embedding

Embedding / Vector Representation

Converting data such as text and images into vectors (arrays of numbers) that preserve semantic meaning. A foundational technology for similarity search and classification tasks.

Context Window

The maximum text length an LLM can process at once. Models with longer context windows can reference more information when generating responses.

Hallucination

A phenomenon where an LLM generates information that contradicts facts as if it were correct. Mitigated through RAG and fact-checking features.

Agent Loop

The process by which an AI agent executes tasks through repeated cycles of 'observe, think, act.' Solves complex tasks step by step.

Function Calling

A feature that allows LLMs to invoke external functions and APIs. Enables tasks that would be impossible for an LLM alone, such as database searches, calculations, and external service integrations.

Multi-Agent

An architecture where multiple AI agents collaborate on tasks. Through role division, it can handle more complex tasks and large-scale processing.

Grounding

Basing LLM responses on external, trusted information sources. A critical technology for preventing hallucinations and improving response reliability.

ROI Calculator

Estimate AI Agent ROI

Quick inputs to estimate cost savings from AI adoption

Enter Current Situation

Number of staff on target tasks

3 people

Hours per person per day on target tasks

2 hrs

Hourly labor cost (incl. benefits)

¥3,000

Current Annual Cost (Target Tasks)

¥4,320,000

= 3 people × 2 hrs × ¥3,000 × 20 days × 12 months

Estimated Savings with AI Agent

Annual Savings (Estimate)

¥2,592,000

* Assuming 60% efficiency improvement

Monthly Savings

¥216,000

Payback Period

10 months

ROI (3-Year)389%

* Free quote and proposal available

* This is an estimate. Actual results vary by use case. Contact us for a detailed quote.