‍KARLI by FiveSquare positions itself as a data-sovereign AI platform with which companies can build and securely operate their own AI assistants and AI agents. The focus is clearly on enterprise requirements: controllable data flows, user and rights management, and hosting in Europe.
The market is full of AI promises. For companies, however, it is less the demo that counts, but the question: How good is the platform in everyday life, how stable are the results and how cleanly can the whole thing be integrated into existing IT and compliance structures?
This test report is therefore about practice: What KARLI can really do today, where the limits lie and why the setup - especially permissions and sources of knowledge - determines success or frustration.
KARLI put to the test: What KARLI can do in principle
KARLI is essentially a platform that is intended to standardize AI use in companies: instead of individual tools per team, there is a central entry point for chat, assistants and agents. This is particularly helpful where many departments are “trying out AI” in parallel and where shadow IT is created in the end.
The platform is based on typical enterprise components: chat for everyday work, a builder for individual assistants and an LLM Hub as a central overview. This is supplemented by user management so that organizations can not only “use” but also “control”.
In practical terms, this means that you can use AI as a productivity tool, but also as a process tool, for example for support pre-qualification, internal knowledge management or standardized text modules.
The functional modules are summarized on the product page as Chat, Assistant Builder, LLM Hub and User Management. (KARLI)
AI chat as a secure alternative for everyday work
The chat is usually the first point of contact. Teams use it for summaries, drafts, reformulations and as a “sparring partner” for concepts, offers or internal communication.
The difference to consumer AI lies in the context: Companies want to avoid employees copying sensitive content into public tools. A company-owned chat with clear guidelines is therefore often the most pragmatic way to start.
Realistically speaking, chat is primarily an accelerator. It reduces start-up costs, makes texts more consistent and helps with structuring. However, it does not replace professional approval when content becomes binding.
The quality depends heavily on which models are stored and how clear the input is. “Do it” produces generic results, concrete goals generate useful designs.
KLARLI - Assistant Builder
KARLI will be exciting where companies not only want “chat”, but also repeatable assistants. The builder aims to build AI logic as a reusable building block: define it once, use it consistently in a team.
Typical examples include HR assistants for standard processes, sales assistants for offer logic or legal assistants for initial orientation. The advantage lies in standardization: When five people complete the same task, they should also use similar quality and similar rules.
In practice, the builder is strong when you have clear process definitions. If a process is already messy, you're otherwise just building a messy agent.
The procedure has proven effective: first simple assistance cases, then gradually more complex workflows. In this way, the platform remains controllable and trust increases.
AI agents for processes instead of just text
AI agents are the next step: not just writing, but supporting task flows. In many companies, this is where AI really delivers ROI—because it reduces routine work.
Good agents start with clear limits. They answer standard questions, collect information and then hand it over to people when decisions or sensitive content are involved.
When agents get too much autonomy, there are two risks: incorrect answers and unwanted data processing. That is why “human in the loop” is often the right middle ground in practice.
An agent is only as good as his knowledge. Without well-maintained sources, it will sound plausible, but its content will fluctuate.
User management and governance as success criteria at KARLI
Enterprise AI rarely fails due to “too few features.” It fails due to a lack of control: Who is allowed to do what, which data is allowed in and how are results checked?
User management is therefore not a secondary issue. It is the basis for AI not to become shadow IT, but to remain a controllable system.
For companies, it is particularly important that authorizations not only “somehow” exist, but also function in everyday life: roles, groups, access to areas of knowledge and clear responsibilities.
When permissions are too broad, AI becomes an oversharing booster. If they are too restrictive, there will be no benefit and employees will evade.
That is exactly why a pilot should always start with curated areas and only then expand.
KARLI put to the test: Why authorizations in SharePoint and more are crucial
There is a typical pattern in Microsoft environments: SharePoint has grown historically and much content is shared too widely. AI is suddenly making this breadth effective because information can be found faster and easier to summarize.
This also applies regardless of whether you upload content directly to KARLI or work via interfaces. As soon as AI gains access to sources of knowledge, old authorization errors become visible.
The most important preparation step is therefore an oversharing check: Which areas contain sensitive content that should not be widely accessible? Which areas are “actually out of date” and should be archived?
It's not glamorous, but it's the quickest way to avoid later discussions. Good rights hygiene is AI readiness.
And it is also a productivity lever: When knowledge is neatly structured, AI answers automatically improve.
Data protection and hosting in Austria
KARLI is clearly positioning itself as a data-secure enterprise LLM solution from Austria. The data protection requirement is explicitly emphasized: hosting in Austria and the DACH region, data in the EU, and a focus on privacy by design. (KARLI data protection)
This is relevant for companies because many AI discussions don't fail because of the model, but because of the question: Where is the data, how is it processed and how can I prove this?
However, a sober classification is important: GDPR-compliant is not a label, but an interplay of contract, configuration, internal rules and a specific use case.
When you process personal data, you need clear policies, which can be incorporated into sources of knowledge. And you need processes for deleting or updating content.
Data protection is therefore not a blocker, but a structural aid: It forces you to operate AI cleanly.
KARLI Voice for transcription and processes
In addition to chat and agent builder, transcription is an increasingly important use case because meetings, interviews and process discussions contain a lot of knowledge. When this knowledge is translated into text, it can be searched for, summarized and further processed more quickly.
KARLI Voice is positioned as a transcription solution, including DACH hosting, 50+ languages and export to structured formats. (KARLI Voice)
This is particularly interesting for companies in areas such as project management, maintenance, administration, customer success or HR. There, knowledge is often created orally and then disappears into calendar appointments.
The greatest benefit is when you have clear rules: When is it transcribed, where is content stored and who can see it? Without these rules, new “text chaos” is created more quickly.
Transcription is a strong input channel for knowledge AI, but it also increases governance requirements.
KARLI put to the test: Quality of results in everyday life — what works well
KARLI is strongest when tasks are clear: summarize, structure, create drafts, answer knowledge questions and support standard processes. These are exactly the tasks that eat up time in everyday office life.
With well-maintained company knowledge, the quality increases significantly. When sources are up-to-date, clear and well-structured, answers are often immediately useful or at least a very good first draft.
This is also helpful in teams: Instead of everyone writing differently, it creates more consistency. This reduces proofreading loops, especially for external texts.
A short quality check proves useful in practice: facts, promises, tonality. These 30 seconds prevent most AI errors.
This allows you to save time without sacrificing quality.
Quality of results — where the limits of KARLI become visible
As with any generative AI, there are limits. It becomes more difficult when content is contradictory or when implicit knowledge is expected that is not documented anywhere.
“binding” content is another borderline case: legal clauses, compliance wording or financial statements. AI can help here, but human approval remains a must.
Even with agents, the more autonomy, the higher the requirements for testing, monitoring and escalation logic. A poorly tested agent creates more effort than benefit.
And: AI can set the wrong priorities if it is not clearly managed. That is why good prompts and clear role models in the company are more important than you think.
The best introduction is therefore gradual. First stable base cases, then more complex automations.
Who KARLI is particularly suitable for
KARLI is particularly suitable for organizations that regard AI not as an individual tool but as a platform. In other words, companies that want to set up several use cases in the long term instead of just providing one chat.
Typical candidates include SMEs and enterprises with data protection requirements, more complex role models, and multiple teams that use AI in parallel. Demand for EU operations and data sovereignty is particularly high in Austria and DACH.
KARLI is also very suitable for public authorities and critical areas where cloud and data residency issues are particularly stringent. Here, “made in Austria” can be a real advantage in terms of internal approval.
KARLI is less appropriate if you only have a very small use case and do not want to establish governance. Then a lean solution is sometimes enough.
A good start is almost always a pilot in a clear area of knowledge. This makes benefits measurable quickly.
Setup checklist for a clean pilot
The first step is curation: Which documents are current, official and relevant? Quality beats quantity, especially in the beginning.
The second step is an authorization concept. Define roles, groups, and clear owners. Without ownership, knowledge management becomes a permanent construction site.
The third step is use case focus. Choose 3 to 5 typical questions or tasks that occur frequently. This allows you to measure success instead of just evaluating “feeling.”
The fourth step is governance: What is allowed in, what is not? How are results checked? How is content updated? These rules make you scalable quickly.
If you start this way, KARLI will not only be introduced, but will also be used sustainably.
Frequently asked questions about KARLI
Is KARLI more of a ChatGPT alternative or an agent platform?
In practice, both, but the platform concept is central: chat as an entry point, assistants and agents as scaling.
Can KARLI be used for sensitive data?
KARLI emphasizes EU operations and privacy by design. Whether your specific use case fits depends on data types, roles and internal rules.
Does this require a lot of IT effort?
A pilot can often start lean. Effort increases as soon as you want to map integrations, large amounts of knowledge or complex role models.
What is the most common mistake during implementation?
Too fast too wide to go. Without authorization and knowledge hygiene, there is oversharing or the quality fluctuates too much.
How is KARLI developing into an agent?
The trend in the market is clearly towards agents who support processes. KARLI is structurally focused on this, because builders and governance components are at the center.
KARLI put to the test: Conclusion for companies
KARLI is an AI platform that takes the business context seriously: governance, data protection, and the ability to set up your own assistants and agents. Anyone who wants to establish AI as a long-term capability in a company will find a clear platform approach here. (incubator)
The greatest benefit comes when you structure your knowledge cleanly and maintain rights consistently. AI then becomes an accelerator for communication, access to knowledge and standard processes.
The limits lie less on the tool than on your preparation. Without clear sources of truth, without ownership and without rights hygiene, all knowledge AI will fluctuate.
If you want to evaluate KARLI, a pilot with a curated area of knowledge is the best route. In this way, you can actually test the quality of results, acceptance and governance.
The KI Company is happy to provide non-binding support with use case selection, pilot design and governance, so that “trying out AI” quickly becomes “using AI productively.”
