On-premise / sLM
Can you adopt AI without sending data outside?
Yes. We build an independent AI environment tailored to the internal data, business systems, and security policies of companies and institutions.
Rather than relying solely on external cloud AI, YEJIN builds AI models, RAG Knowledge Retrieval, a vector database, and specialized AI Agents on the internal servers or dedicated infrastructure of companies and public institutions.
By combining On-Premises, Private Cloud, and a proprietary sLM (Small Language Model), we help organizations use AI securely within their own walls without sending sensitive data outside.
- Block external transmission of internal data
- Own-server and air-gapped operation
- Organization-tailored sLM
- RAG and vector search integration
- Access control and audit logs
- Integration with existing systems
Why On-Premises
The core of AI adoption is not only performance but data control
When companies and public institutions use external generative AI for work, internal documents, customer information, technical materials, and work data can be transmitted to external systems. It is also difficult to directly control model operation policies and how data is retained, which limits security, audit, and regulatory compliance.
Security compliance
Confidential and personal information can be processed under the institution's internal security policy without being sent to external services.
Data sovereignty
The organization directly manages where data is stored, the scope of processing, access permissions, and retention periods.
Regulatory compliance
We configure an operating environment that meets privacy protection, network separation, internal controls, and industry-specific security standards.
Reduced external AI dependence
We secure an independent AI environment that is less affected by changes to specific external APIs or service policies.
Predictable cost
For large-scale, repetitive AI use, we reduce external API call costs and manage infrastructure operating costs in a planned way.
Internal knowledge protection
We prevent your company's know-how, technical documents, and data from being used for external training or third-party processing.
Architecture
We connect models, data, security, and business systems into a single architecture
YEJIN's On-Premises AI is not simply about installing AI models on internal servers. We design your organization's data, user permissions, business systems, and security policy as one integrated architecture.
User and work interface
- Internal portal
- Chatbot
- Work screens
- Admin dashboard
AI Orchestration
- Query-purpose analysis
- Security-level determination
- sLM/LLM/RAG/agent selection
- Process-flow control
RAG and knowledge retrieval
- Internal documents
- Regulations
- Manuals
- Databases
- Vector search
- Evidence discovery
Model layer
- Internal sLM
- Fine-tuned models
- Approved external LLMs
- Task-specific model selection
Data layer
- DMS
- EDMS
- KMS
- ERP
- CRM
- Groupware
- File servers
- Business DBs
Security and operations layer
- User authentication
- Access control
- Encryption
- PII masking
- Audit logs
- Monitoring
sLM
A small language model that understands your organization’s language and work
An sLM is smaller than a large general-purpose AI, making it easier to run on internal servers and to optimize for a company's terminology, documents, and business rules. YEJIN selects a suitable base model according to your data and work characteristics and combines fine-tuning, RAG, prompt policy, and model-lightweighting techniques to build a workflow-specialized sLM.
Model selection
We analyze language, domain, accuracy, response speed, and GPU environment to select a suitable base model.
Domain customization
We optimize the model and knowledge-retrieval structure to reflect the company's terminology, document formats, business rules, and question types.
Fine-tuning and RAG combination
We combine the model's own learning with internal knowledge retrieval to reflect the latest data and the organization's real work standards.
Model lightweighting
We apply quantization, inference optimization, and memory efficiency so it runs reliably even on limited GPU resources.
Performance validation
We evaluate accuracy, response speed, retrieval relevance, hallucination, and security, and set operating standards.
Continuous enhancement
We continuously improve the model and knowledge base by reflecting new documents, user feedback, and changes in work.
Security
We control the entire process, from data storage to AI output generation
Data isolation
We manage data independently through per-organization dedicated servers, dedicated networks, and separated storage.
Encryption in transit and at rest
We apply encryption to data in transit and at rest to prevent external leakage and unauthorized access.
Access control
We limit access to documents, data, and AI features based on the user's department, rank, role, and security level.
PII detection and masking
We configure automatic detection and masking of personal and sensitive information.
Audit logs
We record user queries, referenced documents, data access, AI results, and approval history to support after-the-fact auditing and accountability.
Model security
We detect and block disallowed questions, prompt attacks, internal-information extraction, and abnormal model use.
Network separation
We separate and control the connection scope between the internet network, work network, and AI systems in line with security policy.
Backup and recovery
We regularly back up models, the vector database, configuration information, and logs to prepare for failures and incidents.
Integration
Rather than adding a new system, we connect with your existing work environment
ERP and CRM integration
We connect ERP, CRM, and business databases so that AI can query and analyze the information it needs.
Groupware and electronic-approval integration
We link AI search, document drafting, and work-support features to groupware, electronic approval, and internal portals.
Document management system integration
We connect documents stored in DMS, EDMS, KMS, NAS, and file servers to the RAG knowledge base.
API integration
We connect existing business systems and AI services via standard APIs to automate data queries and work processing.
SDK provision
We provide development tools and integration interfaces so AI features can be applied to your web, app, and business programs.
Legacy system compatibility
Without fully replacing existing systems, we connect only the needed data and features step by step.
Deployment
Deployment models
You can choose according to your organization's security level and infrastructure environment.
Fully air-gapped
The highest-security architecture, where models and data all run on internal servers with no external internet connection.
On-Premises
AI and data run on the company's internal servers, integrating only approved external services in a limited way when needed.
Private Cloud
AI systems and data run independently in a cloud dedicated to the institution, or in a VPC.
Hybrid AI
Sensitive data is processed by an internal sLM while general queries use an external LLM, optimizing performance and cost.
Cloud AI Comparison
How is it different from Cloud AI
Your organization holds the initiative over data and AI operations
Data storage
Typical Cloud AI — External cloud
YEJIN On-Premises · sLM — Internal or dedicated environment
Data control
Typical Cloud AI — Dependent on provider policy
YEJIN On-Premises · sLM — Controlled directly by the organization
Model choice
Typical Cloud AI — Centered on provided models
YEJIN On-Premises · sLM — Chosen to fit work and infrastructure
Internal knowledge integration
Typical Cloud AI — Limited
YEJIN On-Premises · sLM — RAG, DB, and business-system integration
Security policy
Typical Cloud AI — Standard policy applied
YEJIN On-Premises · sLM — Organization-tailored security policy
Audit and traceability
Typical Cloud AI — Limited
YEJIN On-Premises · sLM — Manages query, reference, result, and approval history
Cost structure
Typical Cloud AI — Based on API call volume
YEJIN On-Premises · sLM — Predictable operation centered on own infrastructure
Customization
Typical Cloud AI — Limited
YEJIN On-Premises · sLM — Tailored build of sLM, agents, and workflows
Process
We proceed step by step, from assessment to deployment and operation
- 1
Security and work assessment
Analyze data classification, security policy, business systems, users, and AI adoption goals
- 2
Data and infrastructure analysis
Review documents, databases, networks, and GPU/server environment
- 3
Model and architecture design
Design task-specific sLM, RAG, external LLMs, and system integration approach
- 4
PoC build
Validate accuracy, speed, security, and feasibility using real internal data
- 5
System build
Implement AI models, vector search, admin features, access control, logs, and business-system integration
- 6
Operation and enhancement
Analyze performance, resource usage, search accuracy, and user feedback to continuously improve
Operations
We support stable AI operations even after deployment
Model performance monitoring
We continuously review response accuracy, processing speed, error rate, and GPU resource usage.
Knowledge base updates
We reflect new documents and revised regulations automatically or through an approval process to keep information current.
User and permission management
We manage access to data and AI features in line with department transfers, rank changes, and scope of work.
Log and audit management
We record and review queries, searches, referenced documents, generated results, and approval history.
Security policy updates
We update access controls and protection standards in response to new security threats and changes in the organization's policy.
Model retraining and optimization
We continuously improve the model, prompts, and retrieval structure by reflecting work data and user feedback.
For You
Ideal for organizations that require high security and data control
- Central agencies and local governments
- Public institutions and public enterprises
- Finance, insurance, and fintech companies
- Hospitals and medical institutions
- Manufacturing, defense, and research institutions
- Legal, patent, and professional-service firms
- Large and mid-sized enterprises
- Organizations handling personal information and trade secrets
Frequently Asked Questions
On-Premises is a model in which the AI system is built on a company's or institution's internal servers rather than an external cloud. An sLM is a small language model lightweighted for specific tasks and domains, which can run independently on an internal network or dedicated infrastructure.
It suits companies and public institutions handling data that is hard to send externally, such as personal information, technical materials, trade secrets, research data, and administrative information. It can also be used in finance, healthcare, manufacturing, research institutions, public institutions, and air-gapped environments.
Yes. Using internal servers, a local database, and an sLM, it can run in internal or air-gapped environments with restricted external internet access. That said, the required models, data, and system configuration must be prepared for the internal-network environment before the build.
In an On-Premises and sLM architecture, it can be designed so that internal data is not transmitted to external public AI services. Model calls, document search, and data processing are performed on internal infrastructure to secure control over your data.
Yes. Internal documents, file servers, ERP, CRM, groupware, databases, and work portals can be connected via RAG and vector search. Searchable documents and answer scope can be differentiated by user permission.
For general knowledge and complex reasoning, a large model can be advantageous, but an sLM optimized for a specific industry and task can run faster and more efficiently within its scope. As needed, YEJIN combines an sLM, domestic and global LLMs, and RAG to balance performance and cost.
We size CPU, GPU, VRAM, memory, and storage based on the number of users, model size, response speed, concurrent access, data volume, and security level. We assess whether existing equipment can be used and, if needed, propose a dedicated GPU server configuration.
A single-task PoC typically takes about 4-8 weeks, a RAG build based on internal documents about 2-4 months, and an enterprise-wide On-Premises build integrated with multiple business systems about 4-6 months or more. The exact timeline is set after analyzing data, infrastructure, and security requirements.
Yes. We provide ongoing support for model performance monitoring, data updates, user and permission management, log review, security patches, incident response, backup and recovery, GPU resource management, and cost and performance optimization.
If fast adoption and high scalability matter most, cloud is suitable; if data control and internal-network security come first, On-Premises is suitable. A Private Cloud or Hybrid architecture combining the two is also possible, and we propose the optimal approach based on work criticality and security policy.
Keep your data inside and build AI capability within your organization
YEJIN does not simply install a model; we design your organization's data, business systems, security policy, and infrastructure as one AI architecture. From security assessment to sLM selection, RAG build, system integration, and operational enhancement, we build an independent AI environment tailored to your organization.