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AI implementation for business
Automation and assistants that take over the repetitive work — built into the systems you already use, with the data flow documented.
Where it earns its keep
AI is worth deploying against work that already has a process and repeats often enough to measure. These four are where we see returns most reliably.
Routine enquiries
Sorting, routing and drafting answers to the questions that arrive every week, with a person approving anything unusual.
Documents and invoices
Pulling structured data out of PDFs and scans so it lands in your system instead of being retyped.
Internal search
Answers grounded in your own contracts, manuals and archives, with a citation back to the source document.
Content drafting
First drafts for product copy and descriptions that a person then edits — faster without giving up the final say.
How we work
Measured, not assumed
Scope one task
We start with a single task and a baseline: how long it takes now, how often it happens, and what an error costs.
Build against that baseline
Fixed price for the build. It goes into the tools your team already uses rather than adding another dashboard.
Keep a person in the loop
Anything with a real consequence gets human approval. The system escalates instead of guessing when it is unsure.
Compare and decide
After a month we compare against the baseline. If it is not paying for itself, we say so rather than expanding it.
Worth saying plainly
What AI is not good at
It is not a fix for an undefined process. If nobody can describe how a task is done today, automating it produces faster inconsistency rather than efficiency — that is a process problem first.
It is also not reliable on its own for anything where being wrong is expensive. Language models produce confident text regardless of whether they know the answer, which is why everything we build cites its source and escalates when confidence is low.
And it does not remove the need to understand your own numbers. The value comes from choosing the right task, which is a business judgement, not a technical one.
FAQ
AI implementation – FAQ
Where does AI actually help a business?
In the repetitive work that already has a defined process: sorting and answering routine enquiries, extracting data out of documents and invoices, drafting content that a person then edits, and internal search across your own material. If a task has no clear process, automating it just makes the mess faster.
Do you build chatbots?
Yes, but only where a chatbot is the right answer — usually customer enquiries that repeat. It is grounded in your own documents so it answers from your material rather than inventing, and it hands over to a person when it is not confident.
Will our data be used to train a model?
No. We use providers' business APIs, where inputs are not used for training, and we keep the data flow documented so you know exactly what leaves your systems and what does not.
What does it cost?
Custom applications and automation start at 5,000 €, plus the provider's usage cost, which is typically small relative to the time saved. We quote a fixed price for the build after a short scoping conversation.
Next step
Which task would you automate first?
Describe it in a few sentences and we come back with whether it is worth automating, and what it would cost.