AI integration and automation that actually delivers

AI without the noise

There is a lot of noise around AI. Everyone claims it changes everything, but in practice you mostly have no idea where to start. We keep it concrete. Instead of selling a big AI project, we look for the place in your business where AI already saves time or money today. Sometimes that is a chat feature that triages customer questions, sometimes a model that searches documents, sometimes something else entirely.

The good news is that AI fits neatly into existing software these days. You do not have to rebuild everything. We connect it to what you already have, so your investment stays small and you find out quickly whether it works.

What AI integration actually is

AI integration means building AI into software you already use, instead of putting a separate product next to it. That difference is bigger than it sounds. A standalone AI product asks your team to open an extra tool, retype data, and decide for themselves when to use it. In practice, that stops happening after two weeks.

With good AI integration you barely notice it. The request that comes in is already categorized before anyone opens it. The file already has a summary at the top. The quote is already drafted. Your existing system stays in charge and quietly gains a feature. That is where AI genuinely saves hours, because there is no threshold left to using it.

Where AI automation pays off

AI integration and AI automation are often used interchangeably, but the difference determines how you build it. With integration, AI adds something to work a human does. The system suggests and summarizes, and someone always still looks at it. With automation, AI takes over a process step entirely and the result goes straight through.

Automation delivers more, because that is where the manual work really disappears. But it also demands stricter controls, precisely because there is no longer a person in between to notice a strange outcome. That is why we almost always build the integration form first, let it run alongside the existing work for a few weeks, and only move to full automation when the numbers show the model is reliable enough. That costs a few extra weeks and prevents the scenario where you discover months later that errors have been slipping through unnoticed.

What we use AI for

• Answering or triaging customer questions automatically with a smart chat feature
• Making large volumes of documents or data searchable and easy to summarize
• Automating repetitive manual work, such as entering or classifying data
• Reading incoming mail, invoices and forms and putting them straight into the right system
• Drafting texts, reports or proposals faster while keeping your voice
• Search that understands what someone means instead of only matching keywords

These examples have something in common. They are all work that happens often, requires little judgment and currently costs time from people who would be better off doing something else. That is where AI integration pays for itself. With work that is rare, or where a lot is at stake, the business case is usually a lot thinner and we say so.

AI implementation for SMEs

Most stories about AI are about large organizations with their own data department. For an SME that is of little use. You do not have a team of data scientists, and you have no budget for a year-long program. What you do have is a handful of processes that structurally eat too much time.

That is exactly where AI implementation fits well in an SME, and often better than in a large corporation. You have fewer systems to take into account and you know precisely where it hurts. In practice that means a trajectory of weeks instead of quarters, and an investment in the order of a few thousand to a few tens of thousands of euros instead of six figures.

At SMEs we often run into the same things. Someone who spends an hour every morning retyping orders, an inbox that receives hundreds of requests a week, all sorted by hand, or an archive where nobody can find anything anymore. Each one a problem AI can address without you having to overhaul your entire operation.

How an AI implementation works with us

We work in four steps, and you can stop after each one.

First, find out where it is. A conversation of an hour or two in which we go through where your team loses the most time. Not what is technically possible, but where the hours disappear. Usually two or three candidates come out of that, and one of them clearly stands out.

Then agree on what success is. Before anything is built, we establish what it has to deliver. Does this really save hours a week? How many? Are customers helped faster? Without that number you cannot establish afterwards whether it worked, and you are left with an expensive demo nobody dares to comment on.

Then build small. We make a first working version on a well-defined part of your process. Not a pilot in a separate environment, but something running on real data, because otherwise you still know nothing. This usually exists within two to six weeks.

Finally, measure and expand. We let it run for a few weeks and compare the outcome with what we agreed. If it matches, we expand to the rest of the process. If it does not, you know that after a month instead of after a year, and you have spent a fraction of the budget.

What AI integration costs

Our hourly rate is 110 euros excluding VAT and it is right there on the site. A well-defined AI integration in existing software takes 40 to 120 hours in practice, which is roughly 4,400 to 13,200 euros excluding VAT. The low end is one clear feature in one place. The high end is multiple integrations, your own data that has to be cleaned up first, and a fallback for when the model does something strange.

Then there is one item that does not exist with ordinary software: the consumption of the AI model itself. You pay for that per processed message or document, and for most SME applications it runs from a few tens to a few hundred euros per month. We calculate that upfront based on your expected volume, so you are not caught by surprise afterwards. Our article on what custom software costs explains how such a range is built up.

Where AI projects go wrong

It rarely goes wrong on the technology. These are the places where it does.

Starting with the technology instead of the problem. "We want to do something with AI" is not a brief. It leads to a solution that impresses in a demo and then gathers dust. Start with the process that hurts.

No agreement on what good enough is. A model is never one hundred percent right. If you do not decide upfront what error rate is acceptable and what happens with the cases where the model is unsure, every mistake becomes an argument afterwards.

Involving the people who have to work with it only at the end. AI that changes your team's work without that team having had any say in it gets worked around. That is why we involve users from the first version, even if that means we have to change course along the way.

An AI integration specialist or a broader partner

Some companies look for an AI integration specialist for one well-defined job. Others look for a party that oversees the whole picture, because the AI feature hangs off software that also has to be built and hosted. With us both are possible, and in practice that saves a lot of back-and-forth referrals.

AI integration rarely stands on its own. We often build it as part of a piece of custom software or a web application, running on our own managed hosting. That way you keep everything with one party and there is always someone who sees the whole picture, even when something breaks and it is not immediately clear what caused it.

If you would first like to read how you connect AI to an existing system, we explain it step by step in integrating AI into an existing system.

Frequently asked questions about AI integration

What exactly is AI integration?

AI integration is building AI functionality into software you already use, instead of buying a separate AI product. Your existing system stays in charge and gains an extra feature, for example summarizing documents or automatically categorizing requests.

What does an AI integration cost?

Our hourly rate is 110 euros excluding VAT. A well-defined AI integration in existing software takes 40 to 120 hours in practice, or 4,400 to 13,200 euros excluding VAT. On top of that you pay the consumption costs of the AI model itself, usually a few tens to a few hundred euros per month.

Is AI implementation also worthwhile for a small SME?

Yes, and often sooner than at large organizations. Small companies have fewer systems to take into account and can decide faster. An AI implementation that removes half a day of manual work per week earns itself back just as quickly in a small team as in a large one.

Does our company data stay safe with AI integration?

That depends on the setup chosen and we discuss it upfront. There are models you can run within the EU and even models that run entirely on your own server, so no data leaves. With commercial models we record which data is and is not sent along.

What is the difference between AI integration and AI automation?

With AI integration, AI adds a feature to software that a human works with. With AI automation, AI takes over a process step entirely, without anyone looking at it. Automation delivers more but demands stricter controls, because mistakes are not noticed by a user.

How long does an AI implementation take?

A first working version is usually live within two to six weeks. We deliberately start small, with a trial on a well-defined part of your process, so you see within a month whether the effect is there before you invest further.

Related services

• Having custom software built
• Having web applications built
• Managed hosting for your software
• Custom software in Amsterdam