Not every process is a good fit. The best candidates are high-volume, rule-based and have errors that show up on review. An adoption project consists of these work packages.
How often does the task occur, how rule-based is it, what does a mistake cost, and is there usable sample data? Typical candidates are incoming receipts, delivery notes, messages in a shared inbox, complaints or product descriptions for an online shop.
We assemble a few hundred completed cases whose correct result is already known. Every variant is scored against this set rather than judged by impression.
Initially everything runs apart from live systems, with access only to documents cleared for the test. Only when the scores are right does the tool get connected to your software.
A posting proposal in BMD or RZL, a draft reply in Outlook, a category in the ticketing system, a draft product text in Shopware or WooCommerce. Staff should not need to open an extra window.
Until quality is proven, nothing gets posted or sent without someone agreeing. Documenting this human oversight also fits what the EU AI Act expects. What the regulation means for you specifically is a matter for your legal adviser.
A data processing agreement with the model provider, an entry in your record of processing activities, processing inside the EU and, where needed, a data protection impact assessment. For law firms and medical practices we also check whether a dedicated environment is required.
A short guide, an internal owner and a monthly look at accuracy and cost per item. That way someone notices when supplier layouts change and quality starts to slip.
Timing depends on the use case and the state of your data. After each stage you decide whether to proceed.
Brief conversations with the teams affected, scoring by volume, regularity, cost of errors and data availability.
Two or three model variants compete on the reference set. You see accuracy, time gained and cost per item.
Connection to your programs, then several weeks of operation with the team checking every result.
Documentation, coaching for the responsible person and agreed metrics for ongoing operation.
The most expensive AI is the one nobody touches after three months. That happens when the tool sits beside the real work, when no one is responsible, or when quality deteriorates unnoticed. So every adoption project includes a named person in the business who knows the metrics, gathers feedback and has the authority to decide when adjustments are due.
During the trial your team catches it, and each error feeds into the evaluation. A review step remains afterwards too, often limited to fields where the model itself is unsure. For tax-relevant data we advise against fully automatic posting with no human glance at all.
No. You need someone who understands the process from the business side and can set aside about two hours a week for feedback. We handle the technical side remotely.
Rare one-off cases, weighty decisions with no clear rules and anything lacking usable sample data. Tasks that a simple rule in your existing software could solve do not need AI either, and we will tell you so.
As a project with a scope agreed in advance, or on a time basis at €110 per hour plus VAT following an upfront estimate. Before launch we estimate the model provider's running costs per item and per month.
Programmes such as KMU.DIGITAL, aws funding or schemes run by the federal states have backed digitalisation projects in some rounds. Please check eligibility directly with the funding body. We do not submit applications and make no promise of a grant.
Outline the task, the monthly volume and the program it is handled in today. We will get back to you with an initial assessment.
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