ServicesEnterprise AI
AI for the whole company, on your terms and with your data in-house
Every employee gets AI they are allowed to use, on approved models, with company data that stays in the company and costs you can see per team. We build one platform behind a single gateway, an internal assistant that answers from your documents with each person's permissions, and agents connected to your systems, on your own servers or in your own cloud account. This is usually the next step once staff use ChatGPT on their own and management wants control without banning AI.
How company-wide AI is set up
- 1
We see which AI tools are in use today and what they cost.
- 2
One gateway and approved models are set up in your environment.
- 3
An internal assistant goes live with each person's own permissions.
- 4
Agents and integrations follow, one tested use case at a time.
- 5
Your teams receive the code, tests and runbooks to operate it.
What changes for your business
Company-wide AI changes three things: people get an approved tool instead of private accounts, confidential data can be used at all because it stays in the company, and you see what AI costs per team and per task. In Bitkom's survey of September 2026, 45% of companies that use AI named the concern that company data falls into the wrong hands as a barrier.
- One approved way to use AI for everyone, instead of private ChatGPT accounts
- Offers, contracts and customer files usable with AI, because the data stays on your servers or in your cloud account
- Answers from company knowledge with the source shown, limited to what each person may see
- Spend per team and per task visible, with budgets instead of surprise bills
- One place to switch models when a better or cheaper one appears
- Evidence for your data protection officer and works council in one place
What enterprise AI means in practice
The difference from consumer AI tools lies less in the model than in everything around it. An enterprise system has to know who is asking and what they may see, connect to business systems with their own access rules, leave a record that security and audit can follow, and keep working when a model version changes. A large part of the engineering effort goes there.
More detail
Enterprise AI covers the whole company: the shared platform, the approved models, the internal assistant and the rules everyone works under. Choosing which processes come first and getting the first one into production belongs to AI strategy and rollout, and the servers and model serving underneath belong to on-premise AI. What is left here is the layer that gives every team access on the same terms.
From shadow AI to an internal assistant
Shadow AI means staff using AI tools the company has not approved, often with company data in the prompt. In Bitkom's 2025 survey, 8% of companies reported widespread use of AI tools outside the company's own offering, and 17% reported isolated cases.
More detail
Bans rarely stop it. A better option usually does. Staff get an internal assistant on approved models that answers from your documents with their own permissions, and every model call goes through one gateway. The AI policy is then enforced by the platform rather than by a PDF: approved models only, data classes that may not leave the company, and logging your works council has agreed. Our guide on shadow AI shows how each rule maps to a platform setting.
To see first which tools are in use and what they cost, start with the free AI usage review: one page on spend and quality, built from exports you choose to send.
Would you like to talk this through for your own case? The first call is free and takes 30 minutes.
Book a 30-minute callOne platform and one gateway for every team
A large language model, or LLM, is the kind of model behind tools like ChatGPT. An LLM gateway is one controlled access point to all models, with keys, budgets and logs. Every assistant, agent and tool in the company calls models through it, so IT approves a model once, data protection rules apply everywhere and finance sees spend per team. Our LLM platform page describes the technical build.
More detail
- Approved models only: commercial models in EU regions where allowed, open-weight models on your own servers where not
- Budgets and limits per team, project or use case
- Sign-in and permissions from your existing directory
- Logs in your own store, with retention agreed with your works council
- A model change tested once, then available to every team
Microsoft 365 Copilot or your own AI platform?
Copilot is often enough when your knowledge lives in Microsoft 365, your data may be processed in Microsoft's cloud, and staff mainly need help with mail, documents and meetings. An own platform tends to win when important data sits outside Microsoft 365, in ERP, DMS, file shares or ticket systems, when data has to stay on your own servers, when you want to choose and switch models, or when per-seat licences cost more than usage-based access for the way your people work.
More detail
Many companies end up with both: Copilot for office work, an own platform for company data and processes. We compare them on your own tasks and documents, in the languages your staff work in, with quality and cost side by side, and we earn nothing from either licence. Our guide on Microsoft 365 Copilot or your own AI assistant compares the two in detail.
Enterprise AI solutions we build
We build these systems with your engineers, in your environment, one use case at a time:
More detail
- Enterprise assistants that answer from your documents and systems, with citations and the reader's own permissions
- AI agents that handle routine steps of business cases and pass decisions to people
- AI features in the products you sell, with the serving and infrastructure underneath
- MCP servers that connect approved internal APIs to the AI tools your staff already use
- Private AI: open-weight models served on your infrastructure or in an EU account you control
- Coding agents rolled out across engineering, with shared configuration, cost control and training
Private AI and control of your data
Some data may not leave infrastructure you control. In that case we serve open-weight models on your own servers or in a cloud account and region you choose, sized for the tasks they have to do. Where a commercial model is acceptable, we document the data flow, retention and region so your data protection officer can decide.
More detail
Running models yourself adds operating work: capacity, upgrades, monitoring and on-call cover. We make that cost visible before you choose. A mix can work well, with sensitive or high-volume tasks on a private model and the rest through an approved provider.
Security, governance and the works council
We report usage by team and task, not by individual employee. Legal and compliance decisions stay with your advisers; we prepare the engineering evidence they need.
More detail
- Access checked in the systems behind each tool, not only in the prompt
- Audit logs of tool calls and decisions, without stored credentials
- Approval steps for any action that changes data or sends it out
- Evaluation sets that run before every model, prompt or tool change
- Data flow documentation for your data protection officer, and logging and reporting rules agreed with your works council
A hypothetical enterprise assistant
A manufacturer wants its service technicians to get answers from manuals, past tickets and the parts catalogue while on site. The documents include confidential designs, so the assistant runs on an open-weight model in the company's own data centre. Answers cite the manual page or ticket they come from, and technicians only get answers from documents their role may open.
More detail
Before launch the assistant is tested on real technician questions, including ones the documents cannot answer, and the platform team receives the runbook for adding sources and replacing the model.
How an enterprise AI project runs
We start with one use case and a measured baseline, build with your engineers in your environment, and widen access only after the test results and early production data justify it. Your platform team owns the result: code in your repositories, tests in your CI, monitoring in your tools.
Questions
How do we stop shadow AI without banning AI?
Give people an approved option that is at least as useful: an internal assistant on approved models, with company documents and their permissions, behind one gateway that enforces the rules. Usage then moves to the tool you control, and you can see which teams use what.
Is Microsoft 365 Copilot enough for us?
It can be, if your knowledge lives in Microsoft 365 and your data may be processed there. If important data sits in other systems or has to stay on your own servers, an own platform usually adds more. We test both on your tasks before you buy more licences.
Does a company-wide AI platform pay off?
It pays off when it replaces scattered licences and private accounts, and when it makes company data usable for tasks that save measurable time. We start with one use case and a measured baseline, report usage and cost per team, and widen access only when the figures justify it.
What does the works council see?
On the technical side: which data the platform stores, where it is processed, which fields are logged and for how long, and that usage is reported by team and task rather than by person. We prepare that documentation. The decision stays with you, advised by your data protection officer and lawyers.
What is enterprise AI?
AI built into an organisation's own systems and controls. It knows who is asking, respects existing permissions, connects to business systems, logs what it does and is operated by the organisation's own teams. The model is one part of it.
Can enterprise AI run without sending data to a cloud provider?
Yes. Open-weight models can run on your own servers. Quality and cost depend on the task, so we test the private model against a hosted one on your questions before you decide.
Do you sell an enterprise AI platform?
No. We build on the platforms, clouds and frameworks you already use, and no vendor pays us for recommending theirs.
How do you work with our IT security team?
From the start. Security reviews the access each tool gets, the data flows and the logging before anything goes live, and receives the documentation it needs for later audits.
How does an enterprise AI project start?
With a free 30-minute call with one of our engineers about one use case. We need no access to your systems or data for it. Afterwards we propose a scope and a price.
Related services
AI agent evals
Find out how often an agent gets the job done before it acts in your systems, by checking outcomes, tool calls and how reliably runs repeat.
AI automation
Routine work taken off your teams: processes automated step by step with rules, workflow tools, models and agents, approved by people and measured per case.
AI chatbots
Customers and staff answered faster from your data, with approved actions and a handover to people when it matters.
AI strategy and rollout
AI strategy and rollout that starts with one running, measured system on your own processes and builds the roadmap around its figures.
Data integration for AI
Answers from file shares, SharePoint, ERP and tickets in seconds, for assistants and agents, with permissions carried through and tested.
AI engineers for your team
Extra AI engineering capacity when your team lacks AI skills: experienced engineers who build with you and leave the know-how.
AI evals engineering
Evals built from your own traces show whether each change made your AI product better or worse, with a release gate that stays in your repository.
AI system testing
Before go-live, acceptance tests on your own cases tell you how often your chatbot, assistant or agent is right, in terms your QA team already uses.
Free AI usage review
One page for management on where your AI money goes, whether it pays off and what to change, from exports you choose to share. Free, no system access.
LiteLLM gateway
Budgets and access rules for every team's AI use, through a LiteLLM gateway that is hardened, connected to your identity provider and kept up to date.
AI platform for all teams
One approved route to AI for every team, with budgets, logs and a free choice of model, run on your servers or in your cloud.
System access for agents (MCP)
AI agents that look up and update data in your systems, with narrow permissions, human approval for changes and tests.
On-premise AI
AI on offers, contracts and customer files, with the data staying in your company: own servers or own cloud account, tested before go-live.
Azure OpenAI and Bedrock
GPT and Claude on company data, under the cloud contracts you already have: Azure OpenAI and Amazon Bedrock in your own account, with EU processing and one gateway.
Knowledge assistants (RAG)
Staff find answers in internal documents in seconds, with sources cited, permissions kept and tests on your own questions.
RAG evaluation
An assistant that answers from your documents, tested on retrieval and on answers in each language your users write in, so you know how often it is right and cites the right source.
vLLM on Kubernetes
Open-weight models on your own GPUs, so data stays in house and costs follow capacity you plan, sized from measured load.
AI engineering and architecture
Assistants, voice interfaces and agents in the products you sell, with the serving and retrieval underneath.
Custom model training
A small open-weight model trained for one task and compared with your current model on cost and accuracy.
From the blog
Shadow AI: ban or better option
Shadow AI grows where companies encourage AI without providing it, and bans push it onto private phones. An approved platform that enforces your AI policy works better.
Copilot or your own AI assistant
Copilot covers work that lives in Microsoft 365. Your own AI assistant pays off when answers depend on an ERP, a DMS or data that must stay in-house. Either way, permissions come first.
ChatGPT at work and data protection
Four ways to use ChatGPT-style AI at work, from private accounts to your own servers, and what leaves the company with each. Pick by the most sensitive data people need.
What an LLM gateway does
What an LLM gateway (also called an AI gateway or LLM proxy) takes off your applications and what it leaves to the rest of the platform, with the main options compared as of 7 November 2026.
Talk to an engineer
Give your staff AI they are allowed to use: bring the tools in use today and the data you want to keep in-house. The call takes 30 minutes and needs no access to your systems or your data.
Booking runs on Google Calendar.
When you book a call, you speak to Mickey Graf
He founded Cloudsail and has put machine learning into production since 2017, including AI engineering at trivago and SAP. The call can be in English, German or Polish.
Prefer to write first? Leave your email and a few lines. Mickey reads every message himself and replies by email with times for a call.
Look for the draft in your email program
Send it from there, and Mickey replies in person with times for a 30-minute call. If no draft has opened, write to us at miki@cloudsail.com.