myGPT from Leftshift One is an AI platform from Austria that aims to offer companies a data-secure alternative to public AI tools. In essence, it is about “chat with company knowledge”: myGPT should provide answers from internal sources, accelerate processes and in doing so clearly represent governance and data protection.
Many teams today have the same problem: AI is being used, but not officially. This leads to shadow IT and to sensitive content ending up in tools that are neither approved nor controllable. myGPT addresses exactly this gap and tries to bring AI into a framework that IT, data protection and specialist areas can jointly support.
For this test report, the question is therefore less “does it write well? “decisive, but: How well does myGPT work as a business systemWhen real data, real roles and real processes come into play?
MyGPT from Leftshift One: What exactly can the tool do?
myGPT is a GenAI platform based on Retrieval Augmented Generation (RAG). In simple terms, this means: Instead of “answering freely,” the system should obtain relevant information from your internal sources and derive an answer from this.
In a corporate context, this is the decisive difference to a classic chatbot. You don't just want language, you want usable knowledge from documents, guidelines, wikis, or process descriptions.
Leftshift One explicitly positions myGPT as a “RAG platform for companies” with the aim of making corporate knowledge more usable and reducing search times.
myGPT in everyday work
In everyday life, myGPT typically provides quick benefits for three types of tasks. First: Summarize when employees have to read too much. Second: drafts when too much is written and voted on. Third: Q&A about internal content when information exists but is not easy to find.
For teams with a heavy communication load, this is immediately noticeable. Sales, operations, HR or project teams spend a great deal of time searching for, summarizing and translating content into a form that can be reused.
MyGPT is strongest here as a “draft and finder system.” It provides the first useful status, which humans professionally check and finalize.
Why quality is decisive
If myGPT is really supposed to be good, everything depends on the database. RAG is only an advantage if you have clear, up-to-date and unambiguous sources.
In practice, this is both an opportunity and a risk. The opportunity is: Answers become more comprehensible and less “creative.” The risk is: Contradictions in your documents become visible more quickly and then look like an AI problem, even though it is a knowledge problem.
If you have multiple versions of a policy or five folders with “final_final,” MyGPT will also fluctuate. The fastest quality lever is therefore not prompting, but knowledge hygiene.
myGPT from Leftshift One: What the platform actually brings
myGPT promotes productive GenAI in companies with a clear package of functions: knowledge access, secure use and workflows that should scale in teams.
The function overview describes several components, including source connectivity, controllability, prompt templates, team functions, and security principles.
It is relevant for companies that MyGPT does not just want to be “a chat window.” It's about repeatable use: standards, templates, controllable knowledge spaces and less wild growth.
Why oversharing is the real stumbling block
As soon as you want to integrate internal knowledge, authorization management becomes a core issue. AI makes knowledge easier to find and easier to consume. That's great when rights are clean, and dangerous when content has historically been shared too widely.
Many organizations only notice how much is visible “to everyone” with AI, even though it shouldn't be. myGPT can only be as secure as your rights and content logic.
The pragmatic path is a curated start: an area of knowledge, clear owners, clear roles. Then it is not “everything” that is AI-enabled, but first what is stable and useful.
myGPT as an EU alternative to ChatGPT
Talks with companies often reveal that “We want AI” is not the hurdle. The hurdle is: “We want AI that we can take responsibility for. ”
This includes location and operational issues, but also traceability and clear rules. Companies don't want employees to copy customer data, contracts, or internal figures into any consumer account.
myGPT shows its strength when it is introduced as an official, controlled account. That's when “AI ban plus shadow use” becomes a predictable standard.
MyGPT data protection in the test: What Leftshift One promises
Data protection is not a secondary issue at myGPT. Leftshift One emphasizes that myGPT was developed with a focus on data protection, security and compliance and that data should not be used for model retraining.
This is very clearly communicated in the data protection statement, including the claim of “full control” and “no use for retraining.” Source: https://leftshiftone.com/mygpt-datenschutz/
For companies, this still means that data protection is not automatically created by a tool. You still need internal rules about what can be included in sources of knowledge, how long content is processed and how you handle sensitive data classes.
MyGPT and Strict Mode
A common problem with generative AI is “hallucination”: plausible answers without a reliable source. In its communication about MyGPT, Leftshift One has repeatedly emphasized that there should be mechanisms that reduce exactly that.
One exciting approach is to tie answers more closely to available sources and, when in doubt, to be “not sure” rather than invent something. This is real added value in a corporate context, because “sounds good, it's wrong” quickly becomes expensive there.
When you test myGPT, it is therefore particularly worthwhile to check borderline cases: What happens if the source doesn't provide anything? How does the system react when documents are contradictory?
Why the calculation is different from public AI
Many companies underestimate that enterprise AI is not just a license. It is also setup, governance, and operation.
myGPT communicates prices as flexible, usage-based and scalable. This is typical of enterprise tools, because the actual effort depends heavily on the number of users, amount of knowledge and integrations. Source: https://leftshiftone.com/mygpt-preise/
For you as a test report author, it is important that the ROI does not come from “AI in the company,” but from specific use cases. When you define pilot areas, you can measure time savings and then scale better.
What real examples say about benefits
Real added value is not reflected in features, but in usage. In Austria, there was a recent public report on a MyGPT-related deployment at Verbund, in which an AI assistant (“Polly”) supports employees in everyday life with answers to complex regulations.
This is interesting because it describes exactly the core use case that many companies actually need: faster orientation in internal rules, less research time, less dependence on individual experts. Source: https://elektropraxis.at/energieversorgung/wie-der-verbund-kuenstliche-intelligenz-in-den-arbeitsalltag-integriert/
Such examples are not “proof” that it works the same way for every company. But they show that platform logic is effective in real organizations and doesn't just exist on slides.
What typically works well
In practice, MyGPT is particularly effective for tasks where “design and orientation” already help enormously. Summaries, structured answers to internal questions and draft texts are the typical quick wins.
If your sources are clean, the response quality will also be significantly better. Then myGPT not only delivers text, but also concrete, anchored content from your knowledge base.
A good test is always: Ask the same question with and without connected sources. When the source work is done, the difference is immediately noticeable.
MyGPT limits in the test: Where you should remain critical
MyGPT is also not an autopilot. Legally binding statements, financial figures or compliance formulations continue to require human approval.
It is also difficult when it comes to implicit knowledge. If things have never been documented, MyGPT can't deliver them reliably. That's when the answer becomes either vague or speculative.
And finally, when sources of knowledge are out of date, MyGPT will provide outdated answers. This is not an AI mistake, but a care problem.
Introduction to MyGPT in companies: What works well in pilots
The best introduction is not “everyone can do everything.” The best introduction is a pilot who quickly shows benefits and limits risks.
Start with a team that is measurably suffering: too many queries, too much searching, too many documents. Then choose an area of knowledge that is relatively well-maintained.
Define owners who are responsible for keeping things up to date. Without ownership, quality automatically decreases.
In this way, myGPT is not “introduced”, but “operated”. And that is exactly the difference between an AI game and an AI system.
Why acceptance depends on rules
Many rollouts fail due to communication. Employees ask: Am I allowed to do that? What can I pack in? Who is liable if it is wrong?
If you don't answer these questions, teams either aren't using them at all or they're secretly using other tools. Both are bad.
MyGPT is successful when you have simple rules that work in everyday life: which data does not belong in prompts, how outputs are checked, and which use cases are approved.
Short, concrete guidelines beat any long policy.
Where it can be immediately useful
HR is a typical area where knowledge AI is strong. Onboarding, internal processes, guidelines, benefits, standard answers. This is frequently documented, but difficult to find.
With myGPT, HR can prepare answers faster, formulate content more consistently and inform employees more quickly without HR constantly serving as a “search engine.”
At the same time, HR is privacy-sensitive. That is why a role and authorization concept is crucial here in particular. Not every HR document should be included in a general knowledge base.
myGPT for sales and service: How “Draft + Knowledge” saves time
In sales and customer service, the advantage is often directly measurable: faster writing, faster summarizing, faster access to product knowledge and internal guidelines.
MyGPT can help here in particular if offer texts, service descriptions and standard answers are clearly documented. Answers then become more consistent and less dependent on individual employees.
It remains important: External communication requires approval logic. AI can prepare, but cannot “send it out” automatically.
The “Polly” use case as a blueprint
The network example is interesting because it describes a common enterprise case: Rules are complex, but they are documented.
If myGPT can answer securely via rules, guidelines and internal standards, companies save enormous search time. And they reduce errors that result from ignorance or time pressure.
This is an area where “a little faster” quickly means “much better” because it affects a lot of employees.
Who myGPT is particularly suitable for
myGPT is a particularly good fit for companies that want to officially release AI but aren't happy with public AI. Especially when there are clear requirements for data protection, location and controllability.
It is also very suitable for organizations with a lot of documented knowledge that is difficult to use today. The more knowledge work, the greater the lever.
MyGPT is less appropriate if you only occasionally need AI text and don't want to establish any governance. This is when platform logic is often “too big.”
When platform is better than self-hosted tinkering
Many companies are considering self-hosting with Open WebUI, AnythingLLM, or similar tools. This can work, but requires operational costs: security, updates, monitoring, roles, integrations.
MyGPT is the middle ground for many organizations: enterprise setup, but not completely “build everything yourself”. This can speed up adoption because it involves less infrastructure work.
The right decision depends on whether you have IT capacity and whether you prioritize maximum control or pragmatic implementation.
Common questions about MyGPT from Leftshift One
Is myGPT a real alternative to ChatGPT in the company?
Yes, if you don't just want text, but controlled access to knowledge and an operating model that can be approved internally.
Can MyGPT completely prevent hallucinations?
No generative AI can completely prevent this. But mechanisms such as strong source binding and conservative response behavior can significantly reduce the risk.
Does this require a lot of IT effort?
A pilot can often start lean. The effort increases if you want to connect many data sources, roles and teams. This is normal and can be planned.
How do you get ROI quickly?
With a clear area of knowledge that many people use and tasks that occur daily: summarizing, Q&A, drafts.
MyGPT in the company: Our assessment after the test
myGPT is particularly strong when you use AI not as a toy but as a knowledge system. The RAG focus fits in with what companies really need: answers from their own sources, not just good-sounding texts.
The most compelling perspective is: myGPT helps reduce shadow IT because you offer official, controllable access to AI. This is often more valuable than any individual function.
The limits lie where companies often fail: knowledge hygiene, authorizations, ownership. Anyone who ignores this gets fluctuating quality.
When you're evaluating myGPT, a curated pilot is the best way to get started. One team, one area of knowledge, clear owners, clear rules. In this way, you can quickly see whether MyGPT is measurably saving time in your reality.
