Local AI & On-Premises AI Setup
Beyond borrowing AI — we build your own AI environment.
We design and build AI that fits your company’s data and work environment.
If you want to reduce dependence on external AI services, connect internal documents to AI, or automate repetitive work with AI — now you can build AI that fits your company’s environment.
From hardware selection to AI model installation, performance optimization, RAG setup, and existing system integration — we support the technology needed across the entire adoption process to make AI usable in real work.
01. Why your company needs its own AI
AI is evolving rapidly, but not every company can use AI in the same way.
Using external AI services lets you start conveniently without building infrastructure. However, depending on your business, you must also consider data handling policies, ongoing usage costs, dependence on external services, and system integration.
Local AI and on-premises AI are options that address these needs. You can run AI models on PCs or servers your company manages, connect internal data and business systems, and build an AI environment that fits your organization.
Data Control
Operate AI in your internal environment according to company policy, minimizing or restricting external transmission of sensitive data.
Flexible Operation
Choose AI models that fit your business goals, and change or run multiple models together as needed.
Cost Structure Optimization
Compare usage-based API costs against the upfront investment, power, and maintenance costs of your own infrastructure to design the operation model that fits your work.
Integration with Your Work Environment
Connect AI with internal documents, databases, and business programs to extend it beyond simple Q&A into real work.
02. What are Local AI and On-Premises AI?
Local AI refers to running AI models directly on devices you manage, such as PCs or workstations.
On-premises AI means building and operating an AI runtime environment on servers or data centers your company manages itself.
Both approaches can be used to build a self-managed AI environment; the actual configuration depends on the scale of work and security requirements.
Local AI
Run AI models on a personal PC or workstation. Suitable for development assistance, document summarization, personal work support, and small-scale experimental environments.
On-Premises AI
Build AI models and related services on internal servers so multiple users and business systems can use them together. Usable for organization-level knowledge search, internal AI services, and internal system integration.
Hybrid AI
Use local models and external AI APIs together. Separate handling by data sensitivity and work characteristics, combining the performance of external models with the control of your own operating environment.
Whichever approach you choose, what matters is not picking the latest hardware or the largest model, but comprehensively considering the performance, cost, security, and operational convenience your real work requires.
03. Local AI Setup & Consulting Services
AI Hardware & Infrastructure Consulting
To operate AI models smoothly, selecting hardware that fits your business goals is important. We review conditions such as model size and quantization, required context length, concurrent users, and response speed to propose a suitable system.
- GPU & VRAM capacity review
- System memory & storage configuration
- PC, workstation & server spec consulting
- Power, heat, space & operating environment review
- Analysis of reusing existing equipment
- Phased setup plans based on budget
Rather than buying unnecessarily expensive equipment, we focus on finding a realistic configuration that can perform your target work.
Local LLM Installation & Optimization
We review various AI models, including open-weight models, and configure models and runtime frameworks suitable for your operating environment.
- AI model selection for your business goals
- Model runtime environment setup (Ollama, vLLM, etc.)
- Model download & inference environment configuration
- Memory optimization using quantized models
- Inference speed & response quality validation
- Model replacement & version management plans
- API service configuration as needed
Model performance varies not only by hardware but also by model type, configuration, input data, and concurrent requests. We validate and optimize based on your real usage environment.
RAG-Based Internal Knowledge AI
We connect your documents and work materials to AI to build an internal knowledge search and Q&A system.
- Internal document collection & preprocessing
- Document chunking & embedding pipeline
- Vector-database-based search environment
- AI answer generation using relevant documents
- Providing source documents & answer rationale
- Index updates as data changes
- Document access permissions & search scope design
By leveraging already accumulated documents, we can build a structure that retrieves needed information and uses it in answers, instead of retraining the model on all information.
AI Workflow Automation & Agent Setup
Beyond providing information, we configure AI to use work tools and systems.
- Document summarization & report writing automation
- Data querying & result analysis
- Internal system & API integration
- MCP-based tool connection
- Repetitive workflow automation
- AI task permissions & approval process design
- Task result logging & error handling
Depending on the nature and risk level of work, we design a controllable automation environment that separates tasks AI performs directly from tasks requiring owner approval.
Security & Operating Environment Configuration
Even when operating AI in your own environment, separate security design is needed. We review access permissions for AI models and related services, data storage locations, external communication policies, and task logs to support security configuration suitable for your operating environment.
- Intranet & restricted network environment setup
- AI service access authentication & permission management
- Data storage & external communication policy review
- Model & software version management
- Operation logs & incident response system
- Air-gapped environment installation & update plans
04. Work you can do with Local AI
Internal Document Search & Q&A
Search work manuals, technical documents, product materials, and internal policies, and provide answers based on relevant content.
Document Analysis & Report Writing
Support summarizing long documents, comparing content across materials, and drafting reports in defined formats.
Development & Technical Support
Support development work such as source code analysis, code writing assistance, error root-cause exploration, and technical document search. The actual scope depends on the model’s coding ability and your development environment.
Internal Data Analysis
Connect to databases or business systems to query needed information, and configure it to summarize analysis results or explain them in natural language.
Repetitive Work Automation
Build automation flows that collect materials, analyze content, write documents, or deliver results to business systems according to defined procedures.
Internal AI Service Operation
Configure a common AI service that internal employees can use via web browsers or business programs. Properly designing per-user permissions and data access scopes is important.
05. Various Setup Methods for Your Environment
Personal PC & Development Environment
Suitable for first-time local AI adoption or validating the potential of a specific work task.
GPU Workstation
Usable for work requiring relatively high computational performance, such as development, document analysis, and model experiments.
Internal AI Server
Suitable for environments where multiple employees use a common AI service or operate RAG and business system integration.
Air-Gapped & Restricted Networks
When AI must operate in an environment with restricted external communication, we build considering model import, updates, dependency packages, and data movement & management procedures.
Hybrid AI Environment
Configure some work to be handled by your own models and, when needed, to use approved external AI APIs. You need a policy that clearly separates data allowed for external transmission from data that is not.
06. What equipment do you need?
You don’t necessarily need an expensive GPU server to build local AI.
Simple work support or small model testing can start with an existing PC or a relatively low-spec system. On the other hand, environments using large models, processing long documents, or with multiple concurrent users may need more memory and compute resources.
When selecting equipment, we review the following conditions together.
Rather than always choosing the top spec, we compare from a minimal configuration to an expandable one based on the work you need, optimizing adoption costs.
07. How the setup proceeds
Requirements Analysis
We review your AI adoption goals, key work, data types, security policies, and budget.
Technical Review & Design
We design suitable models, hardware, runtime frameworks, data handling, and system integration structures.
Environment Setup
We install the selected hardware and software and configure the model runtime environment and required application services.
Practical Validation & Optimization
We validate response quality, processing speed, memory usage, and operational stability based on real work scenarios.
Operation & Expansion
We prepare operation guides and management plans, and support RAG, workflow automation, additional models, and user expansion as needed.
08. Frequently Asked Questions
If configured to perform model inference in your own environment, you can design it so input data is not sent to external AI services. However, you should also check external API calls by applications, remote access, updates, and telemetry. The actual level of data protection depends on the overall system configuration.
The AI that fits your company —
let’s design it together.
What you need is not the largest model or the most expensive equipment, but an AI environment that fits your work, budget, and security requirements.
From local AI adoption review to hardware selection, model setup, and RAG & business system integration, we propose realistic adoption plans.