The difference between a demo and everyday work
Almost every company has tried something with AI by now. Someone had a text written for them, someone else had a spreadsheet analysed. It was usually impressive, and little of it stayed. The reason is rarely the technology. It is that the experiment was never connected to the systems people work in every day.
The benefit appears where AI is attached to a concrete task. Incoming email gets pre-sorted, invoices are read out and passed into the system, free text from forms is summarised. Your employee receives a finished suggestion and only has to check it and approve it.
We build this as a fixed part of your software, not as a separate tool next to it. And we clarify beforehand which data is allowed to leave the system. For sensitive content we use models that run on your own hardware.
Use cases with real benefit
Reading receipts and invoices
Supplier invoices, delivery notes and orders are recognised, the relevant fields are pulled out and passed into the system. People only check the result instead of retyping it.
Pre-sorting incoming email
Enquiries are classified by topic, by urgency and by who is responsible, then go straight to the right colleague, with a short summary of the content.
Questions about your own documents
Make manuals, contracts and work instructions searchable. The answer points to the passage it came from, so it stays verifiable.
Support in customer service
Suggested replies based on earlier cases. Your member of staff decides what actually goes out.
Analysing free text
Categorise feedback, fault reports or inspection records automatically, so that recurring problems show up early.
Translation inside the workflow
Bring instructions and safety notices into the languages your team speaks, straight out of the system.
Estimating demand and capacity
Use your historical data to indicate the volumes to expect, as support for your planning.
Where the data is processed
This is the most important question before any AI project, and it is usually asked too late. We settle it at the start, because it decides the effort, the cost and what is legally allowed.
A model run in house
An open language model runs on your hardware or in our data centre. No content leaves your environment.
A model from a provider
Processing runs through an interface at an external provider, with a data processing agreement to match.
The technology behind it
We build the integration mainly in Python. For questions about your own documents we work with vector databases, so that the model answers from checked content instead of writing freely. Where models are to run in house, we set them up on your hardware with Docker.
Integration
Knowledge base
Models
Operation
How we go about it
Narrow down the task
Together we look for a case that comes up often and costs time today. Not the most spectacular one, the most useful one.
Clarify data protection
Which data is involved, where it may be processed and what has to be documented. That comes before the technology.
A small test with real data
A manageable trial shows within a few weeks how good the results really are.
Building it into your software
Only once the quality is right does the function become a fixed part of the system your people already work in.
Watch it and adjust
We measure how often suggestions are accepted and how often they are corrected, then improve on that basis.
What you get out of it
Frequently asked questions about AI integration
Only if you decide in favour of it. There are now capable open models that run entirely on your own hardware. For personal or particularly sensitive data we recommend that route. For uncritical tasks a provider model can be more economical, and then on a contractual basis.
You have to expect that, which is why we build systems so that suggestions can be checked. For questions about your own documents we always name the passage the answer came from. For cases with legal or financial effect, human approval is built in, not optional.
To run a model of your own, yes, usually a server with a suitable graphics card. The size depends on the model and on the number of people using it at the same time. If buying one is not worth it, we can also provide the environment in our data centre.
A clearly defined test usually takes two to four weeks. After that you can say with real figures how often the results were usable. Only on that basis do you decide about the next stage, not on the basis of a presentation.
In the projects we carry out, the point is to take work off people that nobody enjoys doing. Someone who retypes invoices today checks suggestions afterwards and has more time for cases that need experience. How you use the time you gain is your decision.
With a model of your own, essentially electricity and maintenance of the server. Provider models are billed by volume, so the cost rises with use. We estimate this in advance, so that no invoice takes you by surprise.
Let’s start with one task
Large AI strategies usually collapse under their own weight. One clearly defined use case shows within a few weeks if the route is worth taking.