Digitalisation is not a choice. The question is where to start in your company

Not at all. We look at the business as a whole. We assess the digital maturity of your production processes, your logistics and the way you work with people. The aim is to find the places where technology genuinely earns money or saves time.

The audit gives you an objective view of where your company actually stands in terms of technology. It identifies the weak points that are holding you back and points to specific innovation potential in your production or administration. The result is a clear recommendation of technology-independent solutions that will not be dependent on one particular software supplier.

The time frame depends on the size of the business and the extent of the part being transformed. The process of analysis and preparation of the strategy usually takes a matter of weeks. The aim is to create a realistic plan that defines the priority steps, the estimated costs and the specific technological tools needed to achieve your business goals.

Not always. We often focus on modernising and better interconnecting existing systems and processes, or supplementing them with modern tools, AI and cloud solutions. Our strategy is designed so that the transformation is sustainable and builds on what already works and brings value in your company.

By interconnecting digital channels (online sales, mobile applications, social media) you obtain more accurate data about your customers’ behaviour. This allows you to respond more quickly to their needs, personalise offers and simplify the whole interaction with your brand, which leads to greater loyalty and revenue growth.

New technologies are only effective if your people know how to use them and want to use them. Digital transformation changes working habits and organisational culture too. We offer training that explains the benefits of the new tools to employees and teaches them to work with them effectively, which eliminates resistance to change and increases productivity.

With an audit, not with buying software. First we map how the work actually runs, where data is retyped manually and how much that costs the company. This produces a list of priorities according to return – from connecting two systems to automating an entire area. Only then do we address specific tools and the places where it makes sense to involve AI. The initial consultation and the proposed procedure are without obligation.

The price depends on the size of the company and the number of processes and systems being connected. The initial consultation and the proposed procedure are free of charge, so you know in advance what you are paying for. We proceed step by step – we start with the change that has the fastest return, not with a big year-long project. The quotation always includes an estimate of the scope, a deadline and the benefit, which can be measured after deployment.

Before we start we agree on the figures we will track: the time taken to process an order, the number of errors in data, the hours spent on manual work or how long it takes management to receive a report. After deployment we measure them again. With AI functions we also track how many cases are handled without human intervention. Digitalisation that cannot be quantified is hard to justify to management.

At least partly, yes. Language models only work where they have access to data in a usable form – not in ten versions of the same Excel file. In practice the two go hand in hand: when a process is digitalised, the data is unified and completed at the same time, so AI can be connected to it straight afterwards. That is why we do not start by buying an AI tool but by putting processes and data in order.

Most often via API or available connectors – accounting, ERP, e-shop, warehouse, CRM and attendance systems. Where an interface is missing, we handle data transfer another way, for example through imports or an intermediate layer that unifies the data. The aim is for the same item to be entered once and for the other systems to draw on it. Only on top of connected data does it make sense to build reporting for management and AI assistants.

Yes, and it is usually the fastest first step. Receiving and matching documents, approvals, monitoring deadlines, transferring data between systems and regular reports can all be automated. With documents, AI is useful today for extracting data and passing it on. At the same time we leave checking to a human wherever an error would be expensive.

Our experience is different: routine disappears, not people. AI and automation take over retyping data, searching documents and putting reports together – in other words work that nobody enjoys. People move towards customers, checking and decision-making. For this to work, every deployment must also include explanation and training, otherwise the team will go back to Excel.

We deal with security from the start, not only after deployment: access rights by role, backups, encryption and an overview of who works with which data. With AI functions we also monitor where data goes – company information does not belong in public tools without contractual safeguards. Where necessary we choose solutions with data in the EU or operation within your own environment.

Usually yes, just on a different scale. For smaller companies it is not about a big project but about two or three specific changes: connecting the e-shop with accounting, one record system instead of spreadsheets, automatic material for invoicing and AI that handles repeated queries. The return is usually visible sooner than in large companies, because the change immediately affects the whole operation.

That is a common starting point for our cooperation. Usually the software was not what was missing, but rather the setting up of processes, an owner of the change and training for people – which is why the team went back to spreadsheets and e-mails. So first we find out what can be salvaged from the original solution and what is better replaced. Then we proceed in small steps that people can take on board, and we measure the benefit as we go.

Not always. A large part of the work can be handled by available tools, correctly configured and connected. We recommend a bespoke application where an off-the-shelf solution covers only part of the process and you work around the rest with spreadsheets – or when you want to run your own AI assistants on company data. The decision is made after the audit, not in advance.

Most of all where work involves text and documents: searching company guidelines and contracts, preparing answers to repeated queries, extracting data from invoices and orders, checking inputs and summarising long materials for management. The prerequisite is access to up-to-date company data and clearly defined boundaries for what AI may and may not do.

A chatbot answers a query; an agent completes a task. It is given a goal, plans the steps itself, reaches into your systems via API, verifies intermediate results and writes the output where it belongs. Typically it processes an order, prepares material or checks a contract against rules. We always build an agent with a log of steps and with human checking wherever an error would be expensive.

Yes. First we describe the process and decide which steps the agent can handle alone and where a human must decide. Then we build the application on top of your ERP, CRM, e-shop or document store via API. This includes permission management, a log of all steps and a test run on a sample of data before switching to live operation.

We do both. For demand forecasting, classification, customer scoring, anomaly detection or predictive maintenance, classic machine learning models are more accurate, cheaper and easier to explain than language models. We build them directly on your data sets and evaluate them against your existing way of making decisions so that the benefit is measurable.

Yes. From camera images we handle defect detection, completeness checks, reading labels, codes and serial numbers, or counting items. The procedure is always the same: collecting and labelling images, training the model, verifying it on the real production line and only then deploying it. The model can run directly at the machine, so it does not need a permanent connection to the cloud.

We package the model into a service with an API, deploy it on your server, in a private cloud or on an edge device at the machine, and connect it to the systems that are to work with it. We version both models and training data, so a previous version can be restored at any time. We monitor availability, response time, costs and, above all, the accuracy of the outputs.