AI automation
that works for you
We connect artificial intelligence to your processes. AI agents, email automation, document processing — we save you dozens of hours every month.
I want an AI solutionAI is not just a chatbot on your website. We build intelligent automations that process emails, analyze documents, generate content, or orchestrate entire workflows. We connect models like Claude, GPT, or Gemini to your internal systems and create solutions that genuinely save time.
Requests for AI usually arrive before a description of what it should do. Before we design anything, we want one specific activity: who does it today, how often, from what source material and how you can tell it came out right. Without an answer to the last question, there is nothing worth building.
Where automation usually starts
The same briefs come back across industries. Several people retype details from invoices and orders into a system every day. Someone sorts incoming mail and forwards it on. Someone reads long documents only to pull a handful of values out of them. Sales or support keeps answering the same questions, and the answers already sit in company documents. What connects all four is that the procedure can be described and the result checked.
What we deliver
AI agents for workflow automation
An agent does not just describe the task, it carries it out: it reads the brief, finds the data and writes the result back.
LLM integration (Claude, GPT, Gemini)
The model is chosen to fit the task. The interface stays the same, so providers can be swapped later.
Automated email and document processing
An incoming message is routed, the values are pulled from the attachment and stored where they belong.
Content generation and summarization
Longer source material turns into a summary or a first draft. A person approves the final wording.
Answers grounded in your company documents (RAG)
Answers are built from your documents, not from what the model memorized, and each one cites its source.
Custom chatbots and assistants
An assistant for customers and for your own team. It knows when to hand a question over to a person.
How we work together
- Choosing the process We go through the activities that repeat and pick the one with a clear brief and data that is actually available.
- Testing on a sample We try the solution on real data from the past first. You see how often the model succeeds and where it fails.
- Connecting to your systems We connect the automation to the mailbox, the drive or your internal system so nobody has to move data by hand.
- Supervised mode It starts with a person in the loop approving the outputs. The results decide what is allowed to run on its own.
- Running and tuning We watch the success rate and what the model costs to run. Briefs get adjusted from the cases the automation got wrong.
Tech Stack
We use Python and FastAPI because they offer the widest choice of ready-made connections to models and to company systems. The model interface is kept separate, so switching provider does not mean rewriting the logic. Docker makes sure the automation runs on your side, not only on ours.
Where language model automation runs into limits
Output quality follows input quality. When the source material is incomplete or contradicts itself, the model does not fix it; it only restates the error more convincingly. It will fill gaps on its own, so it does not belong anywhere an answer has to be defensible and nobody is checking it. For decisions with consequences a person stays in the loop and approves the output.
The second limit is economic. A process that runs a few times a year, or changes every time, is not worth automating even when the model could technically handle it; maintaining the brief costs more time than the work itself. So we test on past data first and look at two numbers: how often the answer is right, and how long checking the rest takes. When checking takes as long as the original activity, we advise against automating.
See what we have delivered
PortfolioFrequently asked questions
How do I tell which process is worth automating?
Watch how often it repeats and whether you can describe it in sentences. If the procedure differs every time, or the outcome rests on judgement, leave it to a person. The best candidate is monotonous work with a defined result; we start with one such task, not the whole process.
What happens when the model gets something wrong, and who watches it?
Mistakes happen, which is why every automation defines up front what may run on its own and what goes for approval. Outputs are logged, so you can go back and see what the model based a given decision on.
Where does our data go, and is it used to train the model?
Data goes to the provider we agree on: the inputs for that one task, a document or a message. The provider never gets access to your systems. For business work we choose modes where inputs are not used for further training, and we document the provider's terms before we build.
Does it work in our language, not only in Czech and English?
Yes. The models we work with handle the common European languages, and the language of the data does not have to match the language of the interface. What we check on a sample is how the model copes with your terminology and abbreviations; that varies more than the language.
Will you connect it to the systems we already use, and does that still work with your team working remotely?
That is usually the easier part: mailboxes, shared drives and your system's own interface. Where an interface is missing we work with exports, provided the data is machine readable. Being remote changes little; we run under access you grant, in your repository, and you see the same logs we do.
Will customers notice they are talking to AI?
We recommend not hiding it. With a customer-facing assistant we state that the answers are automated and keep an easy route to a person. Hidden automation gets noticed sooner or later, and the damage to trust outweighs what the automation saved.
What is the difference between a chatbot and an AI agent?
A chatbot answers, an agent acts. Besides replying it can create a record, send an email or write a value into a system, so it leaves a change behind rather than text in a window. That is why an agent needs clearly bounded permissions.
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