Process Automation

We use AI where unstructured data has to be processed - survey text, a scanned invoice, the body of an email. Classic automation (n8n, API integration) works where the process is tightly defined: if A, do B. We don’t add AI for its own sake - we pick the tool for the problem. In practice, most deployments combine both approaches: n8n orchestrates the data flow, and an AI model handles what simple rules can’t.

Classic automation executes a defined sequence of steps - if A, do B, then C. An AI agent receives a goal and chooses the steps within agreed rules: it searches sources, processes documents, compares data, and returns a prepared result. The practical difference: automation works best where the process is tightly defined. An AI agent works best where the task requires interpretation and gathering information from multiple sources. We implement both approaches and choose the tool for the problem, not the other way around.

We schedule a 30-minute call to understand the process you want to improve - what’s consuming time, where errors occur, what data flows between systems. Based on this, we prepare a recommendation with concrete steps, a cost estimate, and a pilot proposal. The recommendation indicates which process elements are worth automating first, which tools fit your technology stack, and what measurable results can be expected. The initial call is free and doesn’t commit you to further collaboration.

Automation delivers the greatest results where there are repetitive tasks and data to process - regardless of industry. We’ve implemented solutions in logistics, HR, automotive, insurance, and property management. Typical processes we automate include document flow between departments, report generation from data spread across several systems, data validation and transfer between CRM and ERP, and notifications and escalations in operational workflows. If your team loses hours each week on manual operations that can be described as a sequence of steps - there’s a high probability that automation will speed them up.

We price projects individually after analysing the process, as scope and integration complexity vary between companies. A typical pilot project - covering automation of one process with integration of two or three systems - starts at a few thousand PLN. The pilot allows you to measure the result on production data before deciding on a broader rollout. After an initial call, we prepare an estimate broken down by phases, with measurable KPIs for each phase and a clear billing model. You pay for hours actually worked - not licences or standing retainers for our services.

We typically launch the first pilot 1–2 weeks after kick-off. The pilot covers automation of one process with measurable KPIs, so the result can be assessed on real data. A full implementation with multiple integrations and more complex workflows can take from several weeks to several months - depending on the number of systems to connect, complexity of business logic, and data validation requirements. During delivery we work in phases, launching each automation as it’s ready, rather than waiting for a complete implementation.

That’s exactly why we start with a pilot with measurable KPIs - we define success indicators (e.g. process handling time, number of errors, cost of operation) before kick-off and measure them after deployment. If results don’t confirm the assumptions, we analyse the causes and adjust the approach - changing the tool, modifying the workflow, or recommending a different solution. We don’t push on regardless and don’t charge for an approach that isn’t working. In our experience, the vast majority of pilots confirm their assumptions, because we choose processes with the greatest improvement potential.

Automation doesn’t mean the human disappears. In most deployments we design a human-in-the-loop mechanism - a point where the system hands the decision to a person. The system does 90% of the work (gathers data, analyses it, prepares a recommendation), and the employee makes the final call. This applies above all to areas with real consequences: approving payments, classifying regulated documents, decisions affecting the client relationship. We start with a supervised model and increase autonomy gradually - only once the data confirms the quality of the system’s decisions.

Deployment isn’t the end of the project. After launch, we monitor the key indicators: processing time, error rate, number of human interventions. Based on that data, we optimise the workflow and identify the next areas to improve. An automation that works well today may need adjusting in a quarter - the process changed, a new system arrived, or scale grew. We provide ongoing support and develop the solution as needs change.