No. In most cases, migrating everything first would only slow the project down and increase
costs.
We work with the systems and cloud environments you already have, connecting the data that matters and creating one reliable view across the business.
This allows us to start delivering value quickly, without rebuilding your entire infrastructure first.

The first working prototype solving a specific, defined business problem is typically ready in 4 to 6 weeks. We don’t measure success in features delivered. We measure it in margin improvement, hours recovered, errors eliminated, and cost reduction. Every solution comes with a live dashboard showing exactly what it’s generating. At 100M+ PLN in revenue, even a 1% margin improvement or recovering 15% of your team’s time typically returns the investment within 6 to 9 months. If the numbers don’t add up at the prototype stage, we stop. No blank cheques.

Your data never leaves your infrastructure. We build a private, isolated model instance within your own cloud environment (AWS, Azure or GC) protected by the same security certifications as the rest of your systems. We use RAG architecture: the AI reads and references your documents without permanently learning from them. There is zero risk of your data, contracts or intellectual property surfacing in public models like ChatGPT or Gemini.

Less than you think. We take on the engineering, your team doesn’t need to be involved in the build. What we need is access to the people who understand your business: operations, finance, sales. Short sessions at key moments to define the problem and validate results. We don’t build from scratch, we configure proven cloud foundations for your specific context. Most clients tell us their teams feel relief, not pressure. Because we start by automating the work people hate doing most.

This is the biggest risk when you buy a ready-made solution tied to a single vendor. We build differently. Our architecture is designed so the AI model (the brain of the system) is interchangeable. If a faster or cheaper model emerges tomorrow, we swap it out. The infrastructure stays. The accumulated business knowledge stays. You invest in the data backbone and decision logic, not in a specific version of an algorithm. Your system evolves without starting from scratch.

No. AI takes over the repetitive, low-value work – processing data, reading reports, handling routine decisions. That frees your experts to focus on strategy, relationships and problems that actually require human judgment. The companies that win with AI don’t have fewer people. They have the same people doing more valuable work. 

A person with AI replaces a person without AI. Not the other way around. 

The opposite is true. Every action an AI agent takes is logged, timestamped and visible on a real-time dashboard. Most clients tell us they have more visibility after implementation than before, including into bottlenecks they had no idea existed. You define exactly what the agent handles autonomously and where human approval is required. The control stays with you. It just becomes far easier to exercise.

Management teams often say the same thing after going live: ‘I can finally see what’s actually going on in this company.’ 

We’ll say it plainly. We’re not the right partner if:
● Your business generates limited data volumes or is below roughly 50M PLN in annual revenue. At that scale, simpler off-the-shelf tools will deliver better ROI and we’ll tell you so.
● You’re looking for a magic button. AI requires genuine organisational openness, a willingness to change how decisions get made, not just add a layer of technology on top of old problems.
● The only selection criterion is the lowest price per development hour. We compete on the long-term value our systems add to your EBITDA, not on hourly rates. If those aren’t the same conversation, we’re probably not the right fit.

It depends on where you are. Two scenarios: 

1) If you come with a specific problem, like rising costs, a new implementation or a process that takes too long, we move straight to designing a solution and defining the architecture behind it.
2) If you know you need to do something with your data but aren’t sure where to start, we map the highest-ROI opportunities first. We present concrete proposals that can be implemented within 2 to 3 months, run a workshop to select 2 to 3 priorities, define hard KPIs for each, and build a working prototype on your real data. Then we give you a clear scope and cost for the next phase. 

You make the decision with a working demo in hand. Not a proposal on paper.

Uncontrolled cloud spend catches companies off guard and AI token costs can compound fast if the architecture isn’t designed carefully. From day 1 we implement a FinOps discipline: hard budget limits at infrastructure level that alert or restrict access before overspending occurs, intelligent model routing so routine tasks run on near-free smaller models rather than expensive ones, and advanced caching so you’re not paying to process the same data repeatedly. You get a real-time cost dashboard showing what each AI agent earned and cost, broken down by department and process.
No surprises on the invoice.