Mikis is an AI platform that is clearly aimed at companies and authorities who want to use generative AI with their own data without handing this data over to external cloud AI. The focus is on operating within your own infrastructure, i.e. locally or in a private cloud, including a RAG approach for documents and internal knowledge access. (Mikis)
Anyone looking for “privacy-compliant AI” today is usually not looking for creative poetry. We are looking for a setup that can be approved internally, remains auditable and at the same time brings real productivity gains.
This is exactly where Mikis comes in: less “AI as a gimmick,” more “AI as a controllable system.” But how good is that in everyday life, and what does a company have to do to make it work?
In this test report, you get a realistic classification. What is Mikis already good at today, what should you critically review, and what does a sensible start look like that doesn't end in data chaos?
Mikis as an on-prem AI platform
Mikis describes itself as an on-premise AI platform that makes internal company documents usable via retrieval augmented generation (RAG). This means: The AI answers questions based on your content, instead of just formulating generic answers. (Mikis)
The key point is the operating model. Mikis deliberately wants to run where your data is already stored, i.e. in your infrastructure or in a private cloud, not in just any public AI.
This is important for companies because this is exactly where many approvals fail. Not because of the benefit, but because of the question: “Where does the data go and who controls it? ”
Mikis addresses this question by moving data processing to your environment. This is not a marketing trick, but a strategic architectural decision.
Mikis in the test: A reality check
With many AI tools, the demo looks perfect. In everyday life, however, it is crucial whether the system remains stable in real document landscapes.
Mikis is therefore not primarily a “prompt tool”, but a knowledge and assistance system. The benefits arise when employees can more quickly find, understand and summarize what already exists in the company.
As a result, the success factor is shifting. It is less about “prompting” and more about document quality, versioning, responsibilities and rights.
When you test Mikis, you shouldn't ask, “Does it write nice lyrics? “You should ask, “Does it understand our documents in a way that saves us time without creating new risks? ”
That is a different evaluation standard and is the right one for companies.
Mikis for your own data: RAG and knowledge database without data submission
Mikis's biggest strength is the promise of “own data, own control.” This is particularly relevant for companies that have sensitive documents: contracts, internal guidelines, product data, process descriptions, project documents.
Mikis positions itself as a platform that enables the integration of any language model without the need to transfer data externally. (Miki's KI)
In practice, RAG is there for exactly that: The AI pulls relevant text passages from your inventory, uses them as context and generates an answer from them. This reduces hallucinations and increases comprehensibility.
However, it is important: RAG does not automatically make your content correct. If your content is out of date or contradictory, AI won't solve that problem, it'll make it visible.
This can be unpleasant, but it is an advantage in the long term because it requires knowledge hygiene.
Mikis in the test: What typically works well in everyday life
In practice, on-prem knowledge AIs work particularly well for recurring questions. These include: “How does process X run? ”, “What are the onboarding steps? ”, “Which template applies? ”, “What does Directive Y say? ”
Summaries are also a typical quick-win. Many teams spend time summarizing PDFs, logs, or emails every day before they can even act.
Internal research is another strong area. If employees know that the answer is somewhere in the system but not where, a good RAG chat saves a lot.
These use cases aren't spectacular, but they deliver ROI. And that is exactly the relevant measure in SMEs and in public authorities.
Mikis put to the test: Where the limits are quickly visible
The most important limit is a lack of context. If knowledge is not documented, Mikis cannot reliably deliver it.
A second limit is version conflict. If five documents describe the same process but differently, the AI can answer plausibly and still be wrong.
Binding statements are a third limit. If content is legally, regulatory, or financially relevant, you still need human approval.
It's not a Mikis specific problem. It is a general principle of generative AI, regardless of the provider.
Mikis's strength is not that it replaces responsibility. The strength is that it speeds up drafting and orientation.
Mikis and data protection: Why on-prem doesn't automatically mean “done”
On-premise helps because data stays in your environment. But data protection compliance does not automatically come from “local” alone.
However, you still need to clarify which data is processed, what legal basis applies and which roles exist in the system. Internal processing may also involve personal data and is therefore subject to data protection obligations.
The Austrian data protection authority emphasizes that data protection plays an important role when using AI because personal data is often processed. (DSB Austria)
For companies, this means that even if Mikis runs locally, you need policies. What is allowed in document sources and what is not? Which areas are HR-sensitive? Which content is strictly confidential?
On-prem reduces risks but doesn't replace governance.
Mikis put to the test: Authorizations are the real success factor
With knowledge AI, authorization management is the point where projects tip over. Either it becomes too restrictive, then employees don't use it. Or it gets too wide, which results in oversharing.
Mikis can only work meaningfully if it is clear who is allowed to use which sources of knowledge. In companies, this is often more difficult than expected because document landscapes are growing historically.
Especially when Mikis is intended as an “internal knowledge database,” there must be ownership. Someone has to decide what is Source, what is out of date, and what gets removed.
Without this responsibility, the quality of the response deteriorates over time. This can be planned and avoided, but only if it is taken into account in the rollout.
A good start is therefore a pilot with a clear domain: a team, an area of knowledge, clear sources.
Mikis in practice: examples from references instead of theory
Mikis shows reference projects in which the platform is used as an internal AI assistant and for specific workflows, including in marketing support, confidential business communication or university environments. (Mikis references)
This is relevant because it shows that Mikis is not only intended as a “chat”, but as a modular platform for specific use cases.
Such references are particularly interesting because they typically represent the real distribution of roles: People decide, AI supports, integrates systems.
For companies, this is exactly the right expectation. AI is a tool in the process, not the process itself.
When you write a test report, it's worth taking exactly this perspective: Which processes are getting shorter, which decisions are getting faster, which quality is increasing?
This makes the article tangible without repeating marketing.
Mikis as an alternative to ChatGPT: The difference is the operating model
Many companies compare Mikis with ChatGPT. That falls short.
The relevant difference is: Mikis is geared towards a company with its own infrastructure and data control. This makes AI in companies more predictable.
This is also changing the discussion with data protection and security. Instead of “we hope nothing happens,” you have a system that you can operate, configure, and test.
At the same time, it means: You need IT involvement. On-prem is not “no expense,” but “another expense.”
If the company is prepared to bear this effort, long-term control is usually significantly better.
If the company does not want to bear these costs, a managed EU offering is often the more pragmatic way.
Mikis in the test: introductory steps that work in practice
A good start to Mikis starts with a simple question: “Which 20 questions do we come back every week? ”
Then define the sources from which these questions should be answered. Not “everything,” but “the right thing.”
Then you build roles and rights. Who is allowed to use which domain, who is allowed to import sources, who is the owner of the update?
Then you test in a pilot. Not with all employees, but with a team that really suffers and quickly sees benefits.
Only when quality and governance are right, do you scale.
This creates trust, and trust is the most important factor for adoption in companies using AI.
Mikis in the test: For whom Mikis is particularly suitable
Mikis is a particularly good fit for organizations that have sensitive data and still want to use AI productively. SMEs, public institutions, research, and companies with strict customer requirements are typical.
Mikis is also suitable for companies that want to keep the choice of model flexible. If you don't want to depend on a single AI provider, you're looking for exactly this architecture.
Mikis is also very suitable if you already operate your own server or private cloud infrastructure and IT can operate it.
Mikis is less appropriate if you want “AI immediately without IT.” That's when On-Prem is often too heavy.
A sensible compromise is often hybrid: on-prem for sensitive domains, managed solutions for less critical tasks.
Mikis in the test: Frequently asked questions from companies
Is Mikis really possible on-prem and private cloud?
Yes, Mikis explicitly describes operating locally or in your own private cloud.
Can Mikis answer via internal documents?
Mikis positions itself as a RAG platform for internal company documents, i.e. “chat about your own data.”
Is On-Prem enough to “check off” data protection?
No On-prem helps, but data protection still requires rules, roles, and processes, particularly when it comes to personal data.
What is the best way to start?
With a curated pilot area, clear sources and clear owners. Not with “everything in.”
Miki's conclusion: Is Mikis worthwhile as a data-sovereign AI platform
Mikis is a compelling option when a company wants to use AI but focuses on data sovereignty and infrastructure control. The on-prem and private cloud approach is particularly interesting for organizations that cannot or do not want to release public AI.
The greatest added value is created when Mikis is not introduced as a toy, but as a knowledge and assistance system for specific domains. It then saves search time, reduces queries and speeds up standard work.
The limits lie less in the tool than in the reality of corporate documents. Without clear sources of truth and ownership, quality decreases, no matter which platform you use.
When you evaluate Mikis, start with a pilot that is measurable. One team, one area of knowledge, clear rules. This allows you to quickly see whether Mikis in your area is really saving time while remaining safe.
