Custom AI Agent Development: Process, Cost, Examples

€5,000 to €18,000. Three weeks. An agent that actually runs at the end. That's the short answer to what it costs to have an AI agent developed. The long answer – when it's worth it, how the process works and what comes out of it in practice – is this article.

Buy, rent, or build?

Before you commission an agent, a more honest question is worth asking: do you need development at all – or does an existing tool do the job?

For many standard cases, a platform like Langdock, which gives your team direct access to AI models, is enough – or an automation with Make.com or n8n that connects systems without anyone writing code. Those routes are faster and cheaper – and a serious provider tells you that before selling you a development project.

Custom development is worth it when:

• your process doesn't fit any standard tool – you've tried, and every time you end up bending your workflow instead of the tool.
• several systems need connecting and no ready-made interface exists.
• the agent is meant to become part of your competitive edge – not something every competitor can rent off the shelf.

The tool follows the problem, not the other way around. That's why we work with Make.com, n8n, SmartSuite, Langdock and – where none of those fit – custom code, and settle that question first, every time, before a sprint begins.

What to prepare before the first conversation

You don't need to bring a finished technical solution – that's our job. Still, it helps to ask yourself three questions beforehand:

• Which task costs the most time right now? Not the most interesting one – the one with the most repetitions per week.
• Who does it today, and how long does it take? A rough estimate is fine. It gets precise later, together, in the analysis.
• Which systems are involved? CRM, email, accounting, a spreadsheet – the answer helps decide which tool fits.

With those three answers, the time-potential analysis usually gets a lot more concrete – and you get a solid assessment faster, instead of a generic slideshow about AI.

The process: three weeks, five phases

If development is the right route, it runs as our three-week SCALE² sprint – a fixed sequence of five phases, from kickoff to a running agent:

1. Analysis. We look at your actual process – not the idealized version of it. Which steps does a human do today, what data do they need, where do the systems sit that have to be connected?
2. Concept. We define exactly what the agent takes over, which tool fits, and how many hours per week will realistically come back. That number is put in writing before anything gets built.
3. Development. The agent is built and docked onto your existing systems – CRM, inbox, accounting, whatever your process needs.
4. Testing. The agent runs in parallel with the existing workflow before replacing it. Mistakes show up here – not live, in front of your customers.
5. Go-live and handover. Your team gets a walkthrough, the agent goes into production. Three weeks of post-go-live support are included in the sprint – for questions, fine-tuning and everything that only shows up in real operation.

After three weeks, something real is running in your business. No concept paper, no prototype for the drawer.

What does an AI agent cost?

Most SCALE² sprints fall between €5,000 and €18,000 – as a transparent fixed price, depending on how many systems get connected and how complex the process is. No hourly-rate games, no "starting at", no hidden costs after signing.

The exact price for your scenario comes after the free time-potential analysis – and only if a sprint makes sense for you at all. If a standard tool without any development does the job, we tell you that instead.

Within that range, three factors essentially drive the price: how many systems need connecting, how many different decisions the agent has to make, and how clean the data in your existing systems already is. An agent that handles one clearly defined task in one system sits at the lower end. An agent that connects several systems and makes complex decisions sits at the top.

Two examples from real projects

Theory is patient. So here are two cases where we developed AI agents – with numbers you can check afterwards.

1,000 customer inquiries a day – a travel provider. For a travel provider, we built an AI chatbot connected directly to the booking system. Today it answers up to 1,000 customer inquiries per day – no call-center expansion, no hold queue. Read the case study.

From 30 to 3 minutes per order – an e-commerce business. For a family-run e-commerce company, we rebuilt the entire order backend: processing time down from 30 to 3 minutes per order – minus 90%, across 30,000+ orders a year. That's not trimming at the edges. That's a different company. Read the case study.

Both projects share one thing: no existing system had to go. The agent joined the setup – it didn't replace it.

What counts as success

"It feels faster" isn't a result we accept – and you shouldn't either. Before every sprint, we put in writing how many hours per week the agent should realistically give back – based on the actual current state, not a gut estimate. After go-live, that's checkable: how long did the task take before, how long does it take your team now. We measure ourselves in hours won back. Nothing else – no "sense of innovation", no vague ROI promise, just a number you can verify yourself.

Your systems stay – that's the real risk factor

The biggest reason companies shy away from AI projects is rarely the technology. It's the fear of a migration gone wrong – data getting lost, a team spending three months learning a new system while day-to-day business keeps running.

That's exactly why our approach is "Your systems stay. We dock on." No data migration, no change project, no retraining. Your CRM, your accounting, your calendar stay where they are – the agent learns your processes, not the other way around. And every license and login runs in your name: what we build belongs to you, with or without us as a partner afterwards.

FAQ

How fast will I have a working agent?

After one sprint – three weeks – the agent runs in production in your business. For more complex projects, we split the work into several sprints, each with its own immediately usable result.

Do I have to switch my existing systems?

No. For us, getting an AI agent built doesn't mean introducing something new – it means adding something that was missing. The agent docks onto your CRM, your inbox or your accounting. The systems stay as they are.

What if the agent doesn't run as expected after go-live?

Three weeks of support after go-live are included in the sprint. In that time, we adjust the details that only show up in real operation. And because the hours the agent is supposed to deliver are put in writing before the sprint, success is measured objectively – not by feel.

The next step

You don't have to decide on your own whether an AI agent, an automation or a standard tool fits your case. We settle that together in the time-potential analysis: 45 minutes, free, no sales pitch. More on our approach and further examples on the AI agents service page. Ready? Book your time-potential analysis.