Pricing an automation before studying the process is like quoting a renovation before seeing the building. Two automations that look almost identical on paper can hide completely different levels of complexity, risk, and value. That is why we prefer to start with a different question: what problem are we actually trying to solve, and what is it worth to your organization? Our audit exists precisely to answer that. This first part explains how we evaluate your process: the price ranges, the way the audit works, the mapping, and the measurement of the value at stake. The second part, published next week, then shows how we build the price and what protects you.
Let Us Start With Some Orders of Magnitude
An honest answer to the price question starts with reference points, even broad ones. A simple workflow, one that receives an email, extracts the data, generates a document, and sends it, typically costs between €3,000 and €8,000 to set up. An automation that connects several systems, integrates an AI model, and includes a human validation step most often falls between €8,000 and €25,000. A program that chains several workflows together or transforms a process from end to end is priced after the audit, generally from €25,000. Running the system can then represent between €150 and €1,000 per month, per system, depending on complexity, volumes, and the level of support. A program that combines several workflows accumulates those running costs. Figure 1 puts the three orders of magnitude on a single scale.
These ranges are deliberately broad, because it is the audit that determines where your project sits and why. They reflect our projects as of the date this article was last updated, and we review them at least once a year, since AI model costs and salaries both move. These reference points are indicative and do not constitute an offer: only a signed quote and a signed contract are binding on both parties. If your budget sits well below these figures, we will tell you in the very first conversation.
What Does Our Audit Actually Look Like?
The audit is a product in its own right, with a defined price and a defined deliverable. It is billed as a fixed fee, usually between €900 and €2,500, and that amount is given to you before any commitment. If the project goes ahead, the price of the audit is deducted from the price of the project. The audit usually runs over one to three weeks and takes a few hours of your teams’ time.
The deliverable belongs to you whatever you decide. It contains the process map, the current annual cost of that process along with the assumptions behind the calculation, the recommended architecture, an estimate of the project price and the running costs, and a clear recommendation, including the recommendation not to automate. The document is precise enough to be handed to another provider if you wish. The price estimate it contains is nevertheless inseparable from our terms of delivery: payment conditioned on acceptance criteria, a warranty period, ownership of the code, and reversibility, meaning you can leave and take the system with you. If you use this deliverable to compare quotes, compare those clauses too. A lower price without them is not a saving, it is a transfer of risk onto you.
We Do Not Start With the Technology
When a client contacts us, the initial request often sounds like this:
“We would like to automate the preparation of our case files.” “We receive too many emails and would like to use AI to process them.” “We would like to automate appointment booking.” “Could we generate these documents automatically?”
These requests describe an intention, but not yet a process. Before we talk about artificial intelligence, APIs, or development, we therefore try to understand what actually happens today: who is involved, what information is needed and where it comes from, which software is used, which decisions have to be made, which special cases exist, and what happens when a piece of information is missing or a step fails. A process that looks simple often turns out to involve several systems, manual checks, and exceptions, and the audit is there precisely to make all of that visible.
Mapping the Current Process
Let us take a simplified example. A team receives a request by email, and a member of staff then has to:
- identify the client;
- find their file;
- check several pieces of information;
- pull data from a line-of-business application;
- generate a document;
- send it to the client;
- save a copy in the file;
- check a few days later that the document has come back.
None of these tasks is complicated in itself. But if this process runs fifty or a hundred times a week, it quickly adds up to several hundred hours of work a year. Our first task is therefore to turn an informal process into a clear workflow, by identifying the steps, the people involved, the data and the software concerned, the checks already in place, the exceptions, and the frequent errors.
This step often creates value on its own. It reveals duplicated work, unnecessary steps, or organizational problems that can sometimes be fixed without writing a single line of code.
Measuring What the Process Costs
An automation only makes sense if the problem it solves is worth enough. So we measure how things work today: how many times the task is performed each week, how many minutes it takes, how many people are involved, what that time costs, how much rework is needed, and what the consequences of an error or a delay are.
One point deserves to be said plainly: these figures are then used to size the price of the project, and we would objectively benefit from them looking high. That is why the assumptions about volume and time are never based solely on what we are told in an interview. They are validated together, first from historical data such as system logs, timestamps, and observed volumes, and failing that by observing the process or by sampling real cases, and they are written down in black and white in the audit deliverable. Where the declared figures and the measured figures diverge, the measurements prevail, in both directions: upward as well as downward. You can challenge these assumptions line by line before any quote is issued.
Suppose a process runs 80 times a week, spread across three or four people, and takes an average of 12 minutes of human work. That amounts to 16 hours a week, or roughly 736 hours over 46 working weeks. At an average employer cost of €35 per hour, this process ties up around €25,760 of human capacity every year.
That figure needs to be read correctly. It does not mean the company will save that amount by cutting jobs, and that is almost never the goal anyway. It means the organization currently devotes the equivalent of that amount to this activity, and that automation can redirect the time toward more useful work: client relationships, production, sales, or decisions that genuinely require human judgment.
Time is not the only value at stake, either. Depending on the process, automation also reduces data entry errors, shortens turnaround times, improves traceability, and increases capacity without immediate hiring. In some lines of work, a single error avoided can be worth more than several hours of saved effort.
Deciding What Should Stay Human
Automating a process does not mean removing every human intervention, and that is rarely even desirable. A workflow can be 90 or 95% automated, for instance, while the complex or ambiguous cases are deliberately left to human validation, and that is often the best architecture. Figure 2 shows that split across a hundred files.
Imagine a system that handles 95 files out of 100 automatically, and that flags the five files showing an anomaly so they can go to a member of staff. This approach is often more reliable, simpler, and cheaper than an extremely complex system designed to handle every conceivable situation on its own. The goal is not to remove people from the process, but to remove the repetitive tasks so that human attention concentrates where it genuinely adds value. This conviction has shaped our automation method since day one.
What We Know at This Point, and What Comes Next
At the end of this evaluation phase, we know three things: how your process actually works, what it costs every year, and what should remain in the hands of your teams. What remains is to turn that measurement into a price, and that is exactly what the second part of this article, published next week, covers: why the complexity comes from the integrations rather than from the AI, how our return on investment rule caps the price, a complete worked example, and the guarantees that govern payment, code ownership, and reversibility.
In the meantime, the next step commits you to nothing. A free 30-minute conversation is usually enough to determine whether your process is a good candidate for automation, or whether it is not. Either way, you leave with an answer. Write to us at the bottom of this page, or book a slot directly in our calendar.