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Service

AI Fine-Tuning Support

AI fine-tuning is a service that builds high-accuracy business-specific AI models by further training general LLMs with your proprietary data.

Customize AI models with your company data. Fine-tuning builds industry and task-specific AI models that achieve accuracy impossible with general-purpose AI.

95%+

Business-Specific Accuracy

50%

API Cost Reduction

200社+

AI Support Track Record

Overcome challenges that generic AI models alone cannot solve through fine-tuning.

Challenges

Why Fine-Tuning Is Necessary

Break through challenges that general AI models alone cannot solve with fine-tuning.

Generic AI fails to understand industry terminology

Off-the-shelf AI models cannot properly understand or generate your proprietary terms and industry-specific expressions, making them impractical for real business use.

API costs are skyrocketing

Massive API calls to generic models are inflating costs, making it difficult to scale as a business.

Response quality is inconsistent

Generic AI responses lack consistency, returning different answers to the same question, making it impossible to guarantee business-grade quality.

Reluctance to send confidential data externally

Security concerns about sending customer data and internal confidential information to external APIs are blocking AI adoption.

Methods

Types of Fine-Tuning

We propose the optimal fine-tuning approach based on your objectives.

Instruction Tuning

Task-Specific Instruction Tuning

Train the model on instruction-response patterns for specific business tasks, adjusting it to deliver optimal outputs for business instructions.

Primary Use Cases

Customer support automationAutomated report generationCode generation and review

Difficulty

Medium

Required Data Volume

Hundreds to thousands of samples

Domain Adaptation

Domain-Specific Knowledge Injection

Train the model on industry-specific expertise, terminology, and context, dramatically improving comprehension and generation accuracy in a particular domain.

Primary Use Cases

Legal document analysisMedical terminology comprehensionFinancial report generation

Difficulty

High

Required Data Volume

Thousands to tens of thousands of samples

RLHF

Quality Improvement via Human Feedback

Continuously improve model output quality using human evaluation feedback. Optimize to generate responses aligned with user expectations.

Primary Use Cases

Conversational quality improvementSafety and ethics enhancementBrand tone consistency

Difficulty

Highest

Required Data Volume

Thousands of evaluation samples

Services

Service Details

Covering all stages from data preparation to deployment and operations for fine-tuning.

Data Preparation & Cleansing

We collect, organize, and preprocess the data needed for training. Since data quality directly determines model accuracy, our specialist team performs thorough cleansing.

Data collection and integration
Noise removal and normalization
Annotation design
Training dataset construction

Model Selection & Training

We select the optimal base model for your business requirements and perform fine-tuning with prepared data. We handle everything from GPU infrastructure setup to hyperparameter optimization.

Base model selection
Hyperparameter optimization
GPU environment setup
Training process management

Evaluation & Tuning

We evaluate fine-tuned models from multiple angles. We verify accuracy, speed, and safety, and readjust parameters as needed.

Benchmark evaluation
A/B testing
Safety verification
Performance optimization

Deployment & Operations

We deploy models to production environments and establish stable operational frameworks. We also support monitoring and continuous retraining to maintain accuracy.

API development and system integration
Monitoring design
Continuous retraining
Scaling support

Models

Supported Models

Broadly compatible with major AI models. We propose the optimal model for your requirements.

OpenAI GPT-4/3.5

Closed Models

Official fine-tuning via OpenAI API

Anthropic Claude

Closed Models

Claude API customization and prompt optimization

Meta Llama

Open Models

Open-source Llama 2/3 model training on your own infrastructure

Google Gemma

Open Models

Google's lightweight open model specialized for business use

Mistral

Open Models

High-efficiency Mistral model for domain-specific training

Cohere

Closed Models

Enterprise model customization

Process

Implementation Process

From requirements definition to deployment—phased implementation minimizes risk while maximizing results.

Requirements & Data Assessment

1-2 weeks

Interview business requirements and assess available data quantity and quality. Propose the optimal fine-tuning approach and model.

Available OnlineFree Consultation

Data Preparation & Preprocessing

2-4 weeks

Collect, clean, and annotate training data. Build high-quality datasets to maximize training success rates.

Data CleaningAnnotation

Model Training & Evaluation

2-4 weeks

Perform base model fine-tuning and evaluate accuracy, speed, and safety from multiple angles. Re-adjust parameters as needed.

Benchmark EvaluationA/B Testing

Deployment & Continuous Improvement

Ongoing

Deploy to production and establish operational systems. Continuous monitoring and periodic retraining maintain and improve model accuracy.

MonitoringContinuous Retraining

Pricing

Pricing Plans

From PoC to full deployment and ongoing operations. Choose the plan that fits your goals.

PoC (Proof of Concept)

From ¥1,000,000

Validate effectiveness with small-scale data. A proof-of-concept plan to assess fine-tuning potential.

Requirements interview
Sample data training
Accuracy evaluation report
Full implementation decision support

Recommended

Standard

From ¥3,000,000

Full-scale fine-tuning. End-to-end support from data preparation through model building and evaluation.

Data preparation and cleansing
Model selection and training
Benchmark evaluation
API development and system integration
Deployment support

Enterprise

From ¥8,000,000

Large-scale data and multi-model support. From on-premise GPU setup to RLHF for advanced requirements.

Large-scale data support
Multi-model comparison
RLHF implementation
GPU environment setup
Security design
Dedicated team assignment

Monthly Operations & Retraining

From ¥200,000/mo

Ongoing model operations, monitoring, and periodic retraining support post-deployment.

Model monitoring
Accuracy degradation detection
Periodic retraining
Additional data support
Reporting

Use Cases

Achieving tangible results with fine-tuning across a wide range of industries.

Law Firm

Contract Review AI: Accuracy Improved from 95% to 99%

Legal terminology and contract clause expertise were injected through fine-tuning. Contract review accuracy, which was 95% with generic AI, reached 99% with the custom model.

Accuracy: 95% to 99%Review time reduced by 70%Implementation in 3 months

Manufacturing

Quality Inspection AI: 98% Defect Detection Rate

The model was fine-tuned using proprietary product image data. Subtle defects that were undetectable by generic models can now be identified with high precision.

98% defect detection rate55% inspection cost reductionImplementation in 4 months

Finance

Risk Assessment AI: 80% Reduction in Processing Time

Financial terminology and assessment criteria were trained into the model to automate loan risk assessments. Processing time was dramatically reduced while maintaining accuracy equivalent to manual processing.

80% processing time reductionAssessment accuracy maintainedImplementation in 5 months

FAQ

Answers to frequently asked questions about AI fine-tuning. Feel free to contact us with any other questions.

QWhat is the difference between fine-tuning and RAG?

ARAG is a method that retrieves information from an external database to generate answers, while fine-tuning retrains the model itself with training data. RAG excels at reflecting the latest information, while fine-tuning offers superior response quality, speed, and cost efficiency. We recommend the right approach or combination based on your use case.

QWhat is the difference between fine-tuning and RAG?

AInstruction Tuning can begin with just a few hundred samples. Domain Adaptation typically requires thousands to tens of thousands of samples. If data volume is insufficient, we address this through data augmentation techniques and phased training approaches.

QRAG is a method that retrieves information from an external database to generate answers, while fine-tuning retrains the model itself with training data. RAG excels at reflecting the latest information, while fine-tuning offers superior response quality, speed, and cost efficiency. We recommend the right approach or combination based on your use case.

QCan API costs be reduced?

AYes, significantly. Fine-tuning enables high-quality responses with fewer tokens, reducing API costs by an average of 50%. In many cases, fine-tuning a smaller model can achieve accuracy equivalent to larger models, enabling further cost savings.

QWhat about data security?

AYour data is managed under strict security standards. We support on-premise training and private cloud operations. We also offer the option to completely delete data after training. In addition to NDA agreements, we operate under ISO 27001-compliant management frameworks.

QHow much data is required?

AFor task-specific work, fine-tuned models can achieve an average 20-40% accuracy improvement compared to generic AI. The difference is especially significant in understanding specialized terminology and adhering to industry-specific rules.

QInstruction Tuning can begin with just a few hundred samples. Domain Adaptation typically requires thousands to tens of thousands of samples. If data volume is insufficient, we address this through data augmentation techniques and phased training approaches.

QWhat industries do you support?

AWe support a wide range of industries including legal, finance, manufacturing, healthcare, IT, and retail. The more industry-specific expertise a task requires, the greater the impact of fine-tuning. We have accumulated best practices across industries from 200+ AI support engagements.

For additional questions or to discuss the best fine-tuning plan for your company, please contact us.