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How do you protect data and privacy during training?
Data protection is essential for us – we work only with data you are authorised to use, and we set up the project so that sensitive data never leaves your infrastructure or the agreed secure environment. We comply with the GDPR, sign a non-disclosure agreement (NDA) with all parties, and…How does communication work during the project?
We agree on regular consultations where we show interim results, discuss any changes to the brief and answer questions. If you prefer, we can also keep in touch continuously by e-mail or video call, whichever suits you.What will you need from us at the start of the project?
What helps most is an idea of the problem you want to solve and access to the relevant data – the documents, databases or systems the model is supposed to work with. We take care of the rest, including the technical setup.What hardware do we need for our own AI model?
It depends on the size of the model and how many people will use it. For document data extraction and text classification a single powerful graphics card with 24 GB of memory is enough. For a company assistant over your documents a 32 GB card is a sensible start, and…Who takes care of running, backing up and securing the model?
By agreement we can also run the model after deployment – including backups, monitoring and security measures. If you want to handle operation in-house, we hand over the model with documentation so you can manage it yourselves.Will training a model stop the AI from making things up?
Additional training alone won't; hallucinations are inherent to language models. Reliability is handled differently: by connecting the model to your documents and databases (RAG) so it answers from verified sources, and by thorough testing on real examples. Fine-tuning adds the right terminology and form of answers on top. We combine…Is a local model worse than ChatGPT or Claude?
On the most demanding tasks yes, and there is no point hiding it. The gap is far smaller than it used to be, and for everyday company work – data extraction, summarising, answering over your own data, classification – a well-deployed local model is fully usable today. What decides is…How does training or tuning a model work in practice?
First we clarify the goal together and go through the available data. Then comes data preparation and cleaning, the actual training or fine-tuning, testing on real scenarios and tuning based on the results. Finally we deploy the model and hand it over together with documentation on how to work with…How much does training or fine-tuning an AI model cost?
The price depends on the scope of the task, the size of the model and the amount of data we work with – fine-tuning a smaller model for a specific task costs something different from training a larger model on domain-specific data. During the initial consultation we go through your…What if the local model stops being good enough?
We build the solution so the model can be swapped without rebuilding everything around it. The interface your systems call stays the same, so you can deploy a larger model, move to stronger hardware, or route demanding queries to the cloud and keep the rest local. That combination is what…What is quantization and how much does it degrade the result?
Quantization shrinks a model by storing its internal numbers at lower precision. A model with thirty billion parameters takes about sixty gigabytes at full precision and just under twenty at four-bit, so it fits on a single graphics card. The quality loss at four bits is usually small and at…Can we run the model on a server we already have?
Often yes, especially if it holds a graphics card with enough memory. We audit what you have, measure what will actually run on it and how fast, and only then say whether adding memory, swapping the card or buying a new machine is the answer. Sometimes topping up the existing…
