GPT, Claude and custom ML models in business applications – document processing, data classification and assistants working on your data. AI makes sense where there is a lot of inconsistent text to process, or decisions to make by rules that cannot be written as a simple condition.

Where it genuinely works

Reading invoices, delivery notes and orders from PDFs or email. Categorising products and filling missing attributes from descriptions. Triaging incoming mail and turning it into tasks. Summarising long documents and answering questions from your own documentation, where ordinary full-text search falls short.

How we deploy it

  • The model works on your data, not on general knowledge from the internet.
  • Output is validated against a schema – the application never consumes a reply blindly.
  • Uncertain cases go to manual confirmation instead of a silent guess.
  • Call costs are monitored and capped so the bill holds no surprises.
  • Sensitive data is handled according to what the given provider guarantees.

What AI will not fix

It will not replace missing data or bring order to a process that has none. If the inputs are inconsistent, the model will be inconsistent too – just faster. So with every brief we first assess whether the problem is really a data problem, and whether the source should be cleaned up first.

A practical example is TaskR, where AI processes incoming email and turns it into tasks with context.

Frequently asked questions

Where does our data end up?

It depends on the chosen provider and configuration. For sensitive data we pick solutions that do not use inputs for further training, or run the model so data never leaves your environment. That decision is made at the start, not retrofitted.

How reliable are the results?

On a well-bounded task with output validation, high – but never a hundred percent. So we design processes where uncertain cases go to manual confirmation and where it is possible to see afterwards what the system based its decision on.