On-premises AI · closed environment · Osaka
Put AI to work without sending data outside.
Answering from internal documents is RAG chatbots. This page is the runtime and the security requirement: where the system runs, and how confidential input stays inside your environment.
We assume a model that runs on your servers or inside your network, so a hold caused by leak risk can move forward under stated conditions. Based in Osaka. Serving clients nationwide.
At a glance
On-premises and closed-environment AI runs generative models and RAG inside your environment, without sending confidential data to outside services. Sugureru (Osaka) chooses among fully on-premises, private cloud, and a hybrid split by sensitivity.
- For
- Teams that cannot, or will not, send records to outside services
- Stack
- Local LLM, closed-environment RAG, Next.js / Python / MySQL
- Where
- Based in Osaka. Serving clients nationwide.
Has leak risk put AI on hold?
Internal rules and customer contracts often forbid sending client files, drawings, or meeting notes to an outside service. The usefulness of generative AI is clear; IT still cannot approve a tool whose processing location cannot be explained.
While the official answer stays “no,” people start using public chat windows on their own. There is no rollout, yet near-confidential text still leaves. That gap can be riskier than a managed install.
We do not protect data by banning use. We draw the usable range in the contract and in the architecture. First we hear why sending is forbidden, then we check whether a placement exists that still meets that reason.
A closed environment as an option
Closed means inference finishes on your servers or inside a network you control. Prompts, retrieved documents, and answers do not cross that boundary. Not sending confidential text to an outside API is the starting rule.
The trade against a public cloud assistant is real. You plan model updates yourself and watch GPU and memory yourself. In return, you can show where the data lives — in the contract and on the actual path packets take.
Closing everything is not the goal. We separate what must stay inside from what need not, and we check whether the closed side has enough compute and enough operational hands before we install.
Three patterns
Fully on-premises keeps the model, the documents, and the answers on equipment on your premises. It fits when leaving the internet is almost a contract requirement, or when drawings and personal data are searched every day. You need machines that can infer, plus someone for power, backups, and access. The constraint is that model swaps and incident response stay partly with you.
Private cloud runs the model in a slice reserved for you. You do not own the boxes, and you do not share compute with other customers’ prompts. It fits teams with several offices and a working internal network who cannot place a dedicated GPU. The constraint is a written split of responsibility with the cloud operator.
A hybrid split by sensitivity keeps confidential documents and prompts closed, and sends low-sensitivity drafting elsewhere. It fits when you cannot kit a full on-premises stack, but some records still cannot leave. The constraint is classification: if “confidential” is undefined, people send the same text both ways.
Security and contract
We sign an NDA before we touch real data. Scope of documents, storage, who may access, and deletion or return at the end of the work go on paper.
We do not use entrusted data for any purpose other than that engagement. We do not feed it into extra training, and we do not reuse it as a test set for another client. If you want it written as a procedure, we hand over where working copies live and how they are removed when the work ends.
How we introduce it
We start with the current constraints: forbidden destinations, the network you have, where documents sit, and which jobs actually need search. That conversation usually picks the pattern.
Then we narrow documents and jobs. We do not ingest everything. We start where the effect is easy to see and the sensitivity is easy to explain. A small trial checks answer quality and that traffic does not leave the boundary.
After the trial we design production and build. Handover includes how the corpus grows, access, and incidents. Screens and citations connect to the RAG chatbot design.
Questions we hear
How is this different from the internal search page?
Answering from internal documents with citations is the RAG chatbot page. This page is about where that system runs, and how we keep confidential input from leaving your environment.
Do we have to disconnect from the internet entirely?
Not always. We choose among fully on-premises, private cloud, and a hybrid split by sensitivity. Confidential prompts can stay closed while public drafting uses a separate path.
Do you need our data before a contract?
The first conversation only needs types of data, where they live, and who can touch them. If we later hold real files, we sign an NDA first. We do not reuse data outside the purpose of that engagement.
Closed-environment AI starts with the constraint.
It is fine if “it cannot leave” comes first and the method comes later. First consult is free.