Japanese enterprises and government agencies are among the most cautious adopters of AI globally — and for good reason. Strict data protection regulations, deep-rooted sensitivity around confidential business information, and the high stakes of public-sector data mean that sending queries to external APIs is often not a viable option.
The Problem with Public LLM APIs
When an employee submits a document to a public AI service, that document leaves the organization. Depending on the terms of service, it may be used to train future models. It may be logged, reviewed by support staff, or subject to foreign-government legal requests.
For organizations handling:
- Personal data (subject to Japan's Act on the Protection of Personal Information)
- Classified government information
- Proprietary business strategies
- Medical and financial records
...this is not an acceptable risk.
The inGPT Approach
inGPT deploys a Large Language Model entirely within your own infrastructure. The model runs on-premise or in a private cloud environment you control. Your documents, queries, and responses never leave your network.
The architecture combines:
- Open-source LLMs (such as LLaMA-based models) fine-tuned for enterprise use
- Retrieval-Augmented Generation (RAG) so the model can accurately answer questions about your specific documents
- Standard enterprise authentication (SSO, LDAP) so access control works with your existing systems
Practical Use Cases We've Seen
In our work with Japanese enterprises and local government, the most common use cases have been:
- Internal Q&A over policy documents — HR teams using AI to answer employee questions without exposing the policy documents externally
- Meeting summarization — auto-generating structured notes from transcripts, entirely within the corporate network
- Procurement analysis — searching across past contracts and vendor records to identify patterns and risks
- eKYC support — matching submitted documents against regulatory requirements with AI-assisted review
Getting Started
Deploying a private AI system sounds complex but has become significantly more accessible over the past two years. The key questions to answer before starting are:
- Where will the model run? (GPU server on-premise, or a private cloud VPC?)
- Which documents will you index first?
- Who needs access, and how is access controlled today?
If you'd like to discuss whether inGPT is the right fit for your organization, get in touch with us.