In short
Most AI projects do not founder on the technology. They founder because the starting scope is too broad, because nobody agreed up front what success means, or because the pilot never makes the step into daily operation.
So we work in four steps, with a clear decision point after each one.
1. Scan
We come on site and look with you at where the repetitive work sits. Not on the basis of what a process description says, but on the basis of what your people actually do. That usually produces a list of five to ten possible applications.
From those we pick one together to start with. The criteria are simple: the work comes back often, the data is available, and the result is measurable.
What we need from you
Someone who genuinely knows the process and is allowed to take decisions. That matters more than technical knowledge.
Access to the systems where the data sits, or someone who can arrange it.
A realistic set of examples, awkward cases included.
Clarity on what may happen with your data.
2. SCOPE
Before anything is built, we set out what the system does and above all what it does not do.
- Which steps the agent finishes on its own and where a person approves.
- Which data it may see, per role.
- What happens on doubt or on an unknown case.
- Where the data is processed and stored. More on that.
- How we measure whether it works, with figures you accept in advance.
GOOD TO KNOW
That last point is the one most often skipped and the most important. Without an agreed yardstick, the evaluation after three months becomes a discussion about impressions.
So we agree up front what we count: how many cases the agent handles, how many are corrected, and how much time that saves.
WHERE WE USUALLY START
In practice three applications come up first most often, because they are bounded and show results quickly:
- Making documents searchable, because it changes nothing in your processes.
- Processing incoming documents, because the retyping disappears immediately.
- Checking incoming invoices, because the errors you find there often cover the investment.
3. BUILD AND INTEGRATE
We develop in .NET and connect to your existing systems. In practice most of the time goes into the integration and the edge cases, not into the AI part itself.
We work in short cycles with interim deliveries, so you see what is there and can steer along the way. You test with real data, not with a demonstration.
4. Into production and follow-up
At go-live we usually run in parallel for a period: the agent proposes, your people work as usual, and you compare. You only switch over once the results check out.
Then comes the follow-up. What was proposed, accepted, corrected. Those corrections are valuable, because they point at where the system needs adjusting. An AI application nobody follows up loses noticeable value within six months.
How long it takes
A scan is a matter of days. A first working application is usually a matter of weeks, not years. That is because we deliberately start small: one process, one integration, one clear result.
Projects that take months before anything runs are nearly always projects where too much was promised at once.
YEARS OF .NET EXPERIENCE
PROJECTS DELIVERED
SUCCESSFUL ERP INTEGRATIONS
IN-HOUSE SPECIALISTS
What we do not do
We do not start an AI project when the underlying data is unreliable or when the process itself is not yet settled. In that case AI makes the mess more visible but no smaller. We would rather say that at the scan than after three months.
Why companies choose IDcreation
- The Belgian Defence has been our largest client for over ten years. What meets those security requirements will hold up with you.
- No standard package you have to bend to, but software that follows the way you work.
- Our own hosting in Belgium, with monitoring, backups and a single point of contact.
- Since 1996 we have built in Microsoft technology. One strong foundation makes us fast and thorough.
