AI solutions for business, trained on your data

What AI solutions for business are and where they pay off

An AI solution for a company is software that uses language models, retrieval and computer vision to do part of the work people do today: answer questions, find information, read a document or prepare a draft. Unlike public AI tools, a business solution works on your data, respects access rights and is connected to your systems.

It pays off most in repetitive processes with clear rules: customer support, internal knowledge bases, incoming document processing and lead qualification. AI doesn’t replace the team. It takes over the routine so people can focus on cases that need judgement.

In practice it looks like this: a customer asks about availability and price at 11 pm, and the assistant answers from the current price list and opens an enquiry in your CRM. Someone in accounting photographs an invoice, and the system extracts the supplier, amounts and dates and prepares a record for review. A lawyer asks about a limitation period and gets an answer with the article of law. None of this requires your team to change how they work, because AI runs inside the tools they already use.

Why in-house fine-tuned models instead of public AI services

When a company uses a public AI service, documents and customer conversations go to third-party servers, often outside the EU. For contracts, financial or medical data and client files, that is a risk many organisations simply cannot accept.

That is why we build complete in-house solutions. We use our own models, fine-tuned on your terminology and real examples, and run them in our private EU infrastructure or on your servers. You get predictable costs, control over your data and a model that sounds like your company.

There is a practical difference too. Public services change models, prices and terms regardless of your processes, so answers that were good yesterday may be different tomorrow. With an in-house solution you decide when the model is updated, and every new version passes the same tests on your questions before it reaches production.

• Data never leaves the EU or your network
• No per-message fees or third-party price changes
• The model understands your terms, products and procedures
• The solution stays yours, with no vendor lock-in

RAG and fine-tuning: how AI learns your business

Two techniques work together. RAG (retrieval-augmented generation) means the model searches your documents before answering and answers only from what it found, citing the source. When you change a price list or policy, you update the document and the assistant knows immediately.

Fine-tuning adapts the model itself: writing style, terminology and answer format. We usually use what you already have, such as past support replies, emails and internal guides. Together they produce an assistant that is both accurate and natural, and keeps hallucinations to a minimum because it answers from verified sources.

Quality depends more on data preparation than on model size. A large part of the work is cleaning documents, recognising tables and scanned pages, splitting text into meaningful chunks and running hybrid search that combines meaning with exact terms such as contract numbers or product codes. Finally we rerank the results, so the model only sees the most relevant sources.

How to choose your first AI project

The best first project is small, measurable and visible. Instead of “AI for the whole company”, pick one process where it is easy to measure how much time it takes today and after rollout. In the workshop we score processes against these criteria:

Typical strong candidates are answers to frequent customer questions, triage of incoming email, search across internal procedures and data extraction from invoices or orders. Once the first project shows results, the same foundations, meaning the model, indexes and integrations, are reused for the next process, so every following step is faster and cheaper.

• The task repeats often, at least dozens of times a week
• There are documents or examples the AI can learn from
• Mistakes are catchable, because a person can review a draft
• The result can be measured in response time, tickets or hours

Security, GDPR and on-premise deployment

We design every solution so the AI only sees what the user is allowed to see. Access rights are inherited from existing systems, traffic is encrypted and every action is written to an audit log. Personal data is processed in line with GDPR and local data protection law.

For banks, healthcare, law firms and public institutions we offer on-premise deployment, where the model, documents and indexes stay inside your network. We also help with hardware sizing, since mid-sized fine-tuned models run on one or two GPU servers.

Security also covers how the model behaves. We define what the assistant may do, which actions need human confirmation and which topics it declines. Before launch we test abuse attempts, such as requests for other people’s data or attempts to bypass the rules, and we repeat those tests with every model update.

What AI costs and how to calculate ROI

Building an AI chatbot starts at €800, an assistant with integrations at €2,000 and an enterprise system at €5,000. On top of that comes a monthly hosting and maintenance fee that depends on usage and on whether the model runs with us or on your premises.

ROI is straightforward: how many hours your team spends on the process today, how many it will spend with AI and what a faster reply to a customer is worth. That is why we define metrics at the start and track them during the pilot, so you decide on scaling based on numbers from your own company.

To keep the investment safe, we split the project into phases with a clear outcome. After the prototype you know whether quality meets your expectations, and after the pilot you have real data on savings. Only then does it make sense to extend the solution to new channels, teams or processes.