AI consulting
AI consulting and automation for business
The question is rarely whether AI can do something. It is where in your operation it makes a measurable difference, what your systems have to deliver for that, and who owns the workflow once an agent runs it.
The pilot works. The operation does not keep up
Most AI projects do not fail on the model. They fail because the agent cannot reach the data, because nobody defined what it is allowed to decide and what it is not, and because the process it is meant to automate exists only in the heads of a few people. Swapping a model is an hour of work. Describing a workflow precisely enough for a machine to run it is the actual project.
How we approach AI consulting
Quantify first, build second
We map the workflows that cost time and put numbers on them. Volume, handling time, error rate. Without that baseline there is no way to show later whether anything improved, and that is exactly where pilots die in the budget review.
Settle the data question before the model question
An agent is only as good as its access. We check whether your systems release the data at all, through which interface, and under which permissions. That decides the outcome more often than the choice of model does.
Guardrails before anything goes live
What the agent decides on its own, where that stops, and who reviews the decision afterwards. Those limits belong before production, not in the post-mortem of the first mistake.
One workflow in production, then the next
We put one complete workflow into operation, measure it against the baseline, and only then move on. Ten half-finished pilots are not a programme.
What you end up with
A priority order you can defend
The workflows where AI pays off, ranked by effort and impact, and the ones where it does not, with the reasoning.
Numbers instead of impressions
A baseline before you start and the same measurement afterwards, so the question about value has an answer.
A workflow that actually runs
In production rather than in demo mode, connected to your systems and with defined limits.
A team that stays with it
The people who own the workflow today know what the agent does and where they step in.
Why work with us on AI
We build the systems underneath as well
ERP, data, integration and cloud are ours to deliver. AI consulting that stops at the interface leaves open the part where projects fail.
We also advise against it
For a good number of workflows, clean automation with no model is the better and cheaper answer. We say so instead of putting a model in the middle.
The model follows the task
Anthropic, OpenAI, Google or a model running in your own house. The choice follows the task, the data protection requirement and the cost, not a sales partnership.
More in this area
Common questions about AI consulting
Where do we start?
With a workflow that happens often, can be described clearly, and costs time today. Quotations, invoice checking, order entry, first-line qualification in support. Not the hardest case, but the one that shows the approach holds.
Do we need AI specialists of our own?
Not to run it. You need someone on your side who owns the workflow in business terms and can judge whether the output is right. We take the technical side, and we hand it over so you are not permanently dependent on us.
What about our data?
We settle that before the model choice, not after. Which data may leave your operation and which may not is part of what decides whether a hosted model is an option or a model running in your own house. Where GDPR applies, its requirements apply to personal data unchanged, and the specific assessment belongs to your own data protection review.
How long until we see a result?
The mapping and the priority order are a matter of weeks. A first workflow in production depends on how readily your systems release data. With a clean starting position a few months is realistic, with scattered legacy systems longer.
What if AI turns out not to be the answer?
Then we say so and you save the project. The bottleneck often sits in missing integration or in a process nobody ever defined. We solve both, and both are cheaper than a model placed on top of an unsolved problem.
Where does AI pay off in your operation?
We map your workflows, put numbers on them, and tell you which ones suit AI, which ones do not, and what your systems have to deliver for it.