Apps & AI
If you do not know where to start with AI, this is where you start: we look at what repeats at your company, build the smallest version on your data and integrate it into what you already use. Whatever runs, runs on our own servers in Romania — nothing is sent to OpenAI, Google or any other external provider.
Who this is for
AI usually gets bought as a project. It only gets used if it removes something that repeats.
The same classification, the same extraction, the same small decision, hundreds of times a month. The rule is too complex for a filter and too simple for a person.
Which clients are about to leave, what next month sells, which order carries risk. The answer is in the data and nobody has taken it out.
Contracts or internal policy rule out transfer outside the European Economic Area. That has already ended one conversation about AI, at least once.
It worked in the demo and fell apart on real data. The difference is not the model — it is how well it knows your context.
What you’re losing now
Not falling behind in the abstract. The work you keep paying for, and the information you learn too late.
Classified, extracted, checked, moved from one place to another. Hours paid every month for work that is identical every time.
The client who leaves, the order that slips, the month that comes in under budget. All three showed up in the data weeks earlier.
A generic service is fast to start, and it means your data leaves the company. That cost shows up when somebody asks where it went.
What you get
Six deliverables. The first is the one that saves you from a bad project: the proof-of-concept.
The smallest working version, on your real data, before any large commitment. If it does not hold, that shows here and we stop.
Of your processes, which ones lend themselves to it and which do not, with the effort estimated for each. Including the ones where the answer is “not worth it” — those are the ones that save money.
Classification, extraction, routing, checking. What used to be a rule impossible to write becomes a step that runs on its own.
The model runs on our servers in Romania or on yours. No external AI provider, so no transfer outside your control.
The result lands where it gets used: in the working application, in the report, on the dashboard. Not on a separate screen nobody opens.
A model degrades as the data shifts. It gets tracked, reported and retrained when it needs to be.
How we work
You see the thing working on your own data before there is anything large to sign.
Duration:
You tell us which process repeats or what you would like to anticipate. The shortlist of candidates and the first one to test come out of that.
We build the smallest working version on your data and you use it. If it does not hold, we stop there — you have not bought a project to find out.
Duration:
The model is built, placed on the chosen infrastructure and connected to the systems where the result is used.
Timing depends on how clean the history the model trains on is, and on how many systems the integration has to touch.
Evidence
The process is done by hand, and the fast alternative means sending your data to a provider outside the company.
The process runs on its own, on a model that sits inside the European Union. Your data stays under your control and never feeds the training of a public model.
The comparison describes what changes in how you work, not the measured outcome of any particular project.
What stops you
From the same stage
Applications give the work structure, models anticipate it, agents carry it end to end.
We turn spreadsheets that outgrew themselves into web apps with a real database, multi-user access and no expensive licenses.
Assistants that carry a process end to end — draft quotes, pull data out of documents, handle repetitive requests. With a human in the loop: the agent proposes, you approve.
Next step
Tell us which decision you want to make better. We’ll tell you whether we can help, and how, concretely.