Stop guessing how your team uses AI. Start controlling it.
Bring shadow AI into the light. Kubrius gives staff an approved workspace that knows your business, and gives leadership the routing rules and audit evidence to prove AI is under control.
When work is too sensitive for public AI, Kubrius runs it on your own infrastructure instead. No dead ends, no workarounds.
Built for SMEs that need practical AI control, not enterprise compliance theatre.
AI adoption has already happened. Control has not.
Your staff are using ChatGPT, Copilot, Gemini, Claude and AI features inside everyday software. Some of that use is helpful. Some of it creates risk. Most businesses cannot tell which is which.
What is likely happening today
The problem is not that people are using AI. The problem is that most businesses cannot see what is being used, what data is being shared, or which tasks need a safer route.
- Sensitive documents pasted into public AI tools.
- Client work happening without approval or review.
- HR, finance and legal tasks leaving no evidence trail.
- Nobody sure which AI tools are actually in use.
The real issue is behaviour.
You can write an AI policy, but if users do not have a safe way to use AI, they will keep using whatever tool is fastest. Behaviour changes when the approved route is easier and more useful than the workaround.
- Which AI tools are approved?
- Which tasks are safe, restricted or not allowed?
- Which documents require private routing?
- What evidence exists if a client, board, insurer or auditor asks?
A control layer with somewhere safe to send the sensitive work.
Most governance tools can only watch, warn or block. Kubrius takes a different path: a task too sensitive for public AI doesn't hit a dead end. It runs privately, on your infrastructure.
The approved AI workspace
One place for staff to do everyday AI work, while policy checks, warnings and audit logging run quietly in the background.
- Task-based tools: Company Docs Q&A, Policy Q&A, Customer Email, Contract Review
- Automated policy checks before any task is processed
- Role-based access and usage visibility for leadership
- Audit logs and governance reports for clients, insurers and boards
The private execution path
Local AI infrastructure inside your own environment. RAG and agentic workflows run on your hardware, so sensitive data never leaves your network.
- Local LLM execution for confidential documents, IP and financial data
- Private RAG: cited answers from your own document stores
- Agentic workflows across internal systems, entirely on-premise
- Staff keep the same workspace; routing to Core is automatic and invisible
The Kubrius Portal gives staff approved AI tools for daily tasks.
One approved workspace for company data. Every request gets checked against your rules, routed to the right place, and recorded, with sensitive work going straight to Core.
Every AI task gets a route.
Kubrius reduces unmanaged AI use by making the approved route easier, safer and more useful than random tools.
Approved route
For everyday AI work that is safe to handle through approved tools. Draft emails, summarise public docs, or rewrite notes.
Restricted route
For requests that need a warning, review or clearer policy decision, like HR, finance, or customer information.
Private route: Kubrius Core
For work that should never reach public AI: client contracts, proprietary code, confidential files. Runs locally on Core, in your environment.
Work faster with an AI that already knows your business.
A simple place to use AI without guessing which tool is allowed.
- Zero-friction context
- Direct document Q&A
- No guessing which tool is safe
- Approved, reliable workflows
Visible usage, controlled by policy.
A practical way to see, manage and evidence AI use across the business, with a private execution path when workflows demand it.
- Usage visibility and policy control
- Review prompts and audit evidence
- Local models, private RAG and agentic workflows via Kubrius Core
- Role-aware access and controlled retrieval
Built for firms where client trust is the business.
“Our fee earners were already using AI. We just couldn't prove it was safe. Kubrius gave us one approved workspace, and contract work now runs on our own hardware. When a client asked how we handle their documents with AI, we had an answer in writing.”
You'll be talking to me, not a sales team.
“I spent six years as an infrastructure engineer moving companies into the cloud. Now I help them decide what should come back out. When the AI boom hit, I watched businesses try to govern behaviour with a PDF policy while staff pasted client data into public chatbots. Not maliciously. It was simply the fastest way to get work done.
I built Kubrius on a simple engineering truth: you cannot control behaviour with a policy. You control it with a better workflow. If your team is sneaking out the back door to use public AI, the answer is a front door that is actually better.”
The experience behind Kubrius
- Over 20 years in IT and DevOps, building and running production infrastructure: CI/CD, containers, hybrid and cloud environments.
- Deployed machine learning models into production before "MLOps" was a job title, and learned model governance the hard way.
- Advising SMEs on practical, secure AI adoption since 2019, seeing the same shadow AI problem on repeat before building the fix.
- Ran my own small business for over a decade. I know what it means to wear every hat and guard every client relationship.
- A decade in regulated financial services before that, where client confidentiality isn't an abstraction.
And I explain all of it in plain English. No jargon, no hype, no 50-page strategy decks.
Start with the AI Control Scorecard.
Before buying another AI tool, find out where AI is already being used, what data may be exposed and which controls should come first.
The scorecard gives you a clear starting point: current usage, priority risks and the first approved workflows worth putting into Kubrius.
Frequently asked questions.
What is shadow AI, and how does Kubrius stop it?
Shadow AI occurs when employees use unvetted, public AI tools for company work, exposing sensitive data. Kubrius stops this not by blocking access, but by providing an approved, context-aware AI workspace that is more useful than public workarounds. We replace the need for shadow IT with an authorised front door.
How is Kubrius different from ChatGPT Enterprise or Microsoft Copilot?
Those are individual productivity tools; Kubrius is an operational control layer. Public tools leave policy compliance up to the user. Kubrius wraps AI access in automated policy checks, routing and audit logs. It also adds something they can't match: a private execution path on your own infrastructure via Kubrius Core.
Does Kubrius train its AI models on our company data?
No. Data routed through the Kubrius Portal is never used to train foundation models, and work routed to Kubrius Core is processed entirely on your own infrastructure, inside your network.
Do employees need to know how to prompt or choose AI models?
No. Kubrius is built around task-based workflows. Users pick the business task (e.g., "Summarise Contract"). Kubrius selects the underlying model, injects the right company context, and applies your policy routing before the request runs.
What happens if a user tries to process a confidential document?
Kubrius checks the request before processing. Depending on your rules, it warns the user, blocks the action, or re-routes the task to Kubrius Core, your private local infrastructure. The event is logged for audit review either way.
What is Kubrius Core?
Core is private AI infrastructure deployed in your own environment. It runs open-weights language models, private document retrieval (RAG) and agentic workflows entirely inside your network. Built for client files, proprietary IP and financial data that shouldn't reach third-party cloud APIs.
Do we need to run Core to use Kubrius?
No. Most businesses start with the Portal alone. Core matters once specific workflows justify local infrastructure: legal review, code analysis, executive data. The Scorecard tells you if and when that's you.
Bring AI use out of the shadows.
If your team is already using AI, the question is no longer whether your company should adopt it. The question is whether you can see it, manage it and prove it is being used responsibly.
Start with an AI Control Scorecard. We will identify your first control priorities and whether the Portal, Core, or neither is the right fit.
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