Generative AI Without Sending Your Data Outside — Launch of Secure Local LLM Deployment and Operations Support

Harness generative AI without sending confidential data outside your organization — we provide end-to-end support for local LLMs that run in on-premise and air-gapped environments, from deployment to operations.

Amatsukaze Inc. has launched its secure local LLM (on-premise AI) deployment and operations support service, enabling organizations to leverage generative AI without sending confidential data to external services. The service provides end-to-end support spanning model selection, infrastructure setup, RAG integration, and operational design — answering the needs of organizations that want to adopt generative AI but cannot let their data leave their walls.

The Current Landscape: Generative AI Goes Mainstream as Data Sovereignty Concerns Deepen ​

The business use of generative AI is moving from the proof-of-concept phase into full-scale adoption. At the same time, cloud-based LLM APIs transmit prompts and business data to the service provider, which means the risk of leaking confidential information, personal data, or customer data cannot be fully eliminated. In tightly regulated sectors such as finance, healthcare, manufacturing, and the public sector, the use of external cloud services is often restricted outright.

Awareness of "data sovereignty" has risen to become a board-level priority worldwide. In Europe, the general-purpose AI obligations of the EU AI Act entered their enforcement phase in August 2025, and together with the GDPR they make it effectively difficult to route customer data freely through LLM APIs hosted abroad. In Japan as well, interest in options that keep data in-house is growing rapidly from the perspective of personal data protection and governance.

Against this backdrop, throughout 2026 major system integrators and manufacturers have launched on-premise LLM support services one after another, and the market has taken off. The technology, too, provides a strong tailwind. Google's "Gemma 4 12B," released under the Apache 2.0 license in June 2026, runs locally on a typical 16GB laptop. With a steady stream of commercially usable open-weight models — the Qwen3 series, OpenAI's gpt-oss, and the DeepSeek family — we have entered an era in which high accuracy can be achieved even locally.

Why Local LLMs—And Why Now ​

There are three main reasons local LLMs are being chosen.

• Security (no external data transmission): Processing is completed entirely within your internal network or air-gapped environment, so no data is sent outside. This minimizes the risk of information leakage by design and satisfies a prerequisite for regulatory compliance.
• Cost predictability: There is no usage-based billing as with cloud APIs, so costs do not spike as usage grows. This makes mid- to long-term budgeting easier and supports investment decisions with company-wide rollout in mind.
• Customization: You control the model and its versions in-house, allowing flexible optimization (fine-tuning and RAG integration) tailored to your business processes and proprietary data.

That said, even as open-weight models have grown more capable, a new challenge has surfaced: the difficulty of deploying them in-house. Selecting and procuring GPU hardware, choosing the right model for the use case, building the inference infrastructure, and designing for stable operations all demand a high level of expertise. This is precisely where a knowledgeable partner adds value.

What Outcomes You Can Expect ​

Deploying a local LLM can deliver the following benefits.

• Generative AI for confidential workloads: Applications you previously abandoned because data cannot leave your organization — design document review, internal knowledge search and summarization, customer inquiry handling, code generation, and more — become possible with generative AI.
• A stable cost structure: Because inference cost can be treated as a fixed cost of your in-house infrastructure, you achieve a predictable cost structure that is not affected by usage volume.
• Higher accuracy for specialized tasks: By using RAG over your proprietary data and small language models (SLMs), you can derive highly accurate, business-optimized answers that are hard to reach with general-purpose models.
• Operation in offline / closed environments: Generative AI can be used even on sites with restricted networks or in environments that do not permit external connections.

How We Make It Happen ​

Amatsukaze adopts a low-risk "small start" as its core principle, supporting you in stages from proof of concept (PoC) through full deployment and operations.

• Model selection and validation (PoC): From open-weight models such as Qwen3, gpt-oss, Gemma 4, and the DeepSeek family, we select the optimal model balancing use case, accuracy, cost, and required hardware. We verify the impact in a small validation environment before moving to full deployment.
• Inference infrastructure setup: Using inference engines such as vLLM and Ollama, we build infrastructure suited to on-premise, closed-network, and air-gapped environments.
• RAG and tool integration: Through agentic RAG, GraphRAG, and integration of tools and data via the Model Context Protocol (MCP), we implement practical AI grounded in your own knowledge.
• Phased scaling: After validating the impact, we scale up without strain to a production-grade setup — including adding GPU servers and designing the overall infrastructure and operations.
• AI governance: We embed guardrails, evaluations (Evals), audit logs, and LLM observability to support safe and explainable AI use.

Why Amatsukaze Is the Right Partner ​

Deploying a local LLM is not simply about running a high-performance model; success hinges on whether you can design infrastructure, AI, and operations as a single integrated whole. Amatsukaze's strength is a structure that lets us work alongside you across all three.

• Infrastructure operations expertise: We bring knowledge cultivated through the design of high-load, high-transaction systems and the construction and operation of robust infrastructure. We support everything from GPU resource design to stable operations — grounded in real operational experience, not just theoretical knowledge.
• AI / LLM engineering: We have hands-on capability with the latest open-weight models, RAG, agentic AI, and MCP, translating them into AI that delivers real business value.
• End-to-end partnership: We support everything from model selection and PoC through production operations and governance under one roof. Rather than "deploy and done," we work with you to build a mechanism that keeps generating value.