AI integration for businesses: LLMs and data that work in everyday operations
We bring large language models (LLMs) to the places in your company where they genuinely take work off people's hands: document processing, internal knowledge search and assistants for your staff. We also build the data pipelines and reports that decisions rely on, and we move machine learning models into production properly. Data protection and the requirements of the EU AI Act are part of our thinking from day one.
Where AI projects tend to stall
- A ChatGPT prototype was convincing, but nobody knows how to turn it into a dependable system that works with your own data and is embedded in existing workflows.
- Knowledge is scattered across manuals, contracts, emails and tickets. Staff spend ages searching and keep asking the same questions.
- Incoming invoices, delivery notes or applications are still typed in and checked by hand, even though their content is largely structured.
- Data sits in ERP, CRM, spreadsheets and line-of-business applications that don't talk to each other; reports take effort to produce and are out of date by the time they appear.
- Uncertainty about data protection and regulation: which data may be sent to a model provider, where is it processed, and what does the EU AI Act mean for your use case?
- A trained ML model works in a data scientist's notebook but not in production. There is no versioning, no monitoring and nobody owns it.
How we approach LLM integration
Clarify the use case and the benefit
During requirements analysis we work out together where AI saves measurable effort and where a conventional solution fits better. We agree how success will be measured, which data is needed and what risks exist.
Review data and data protection
We clarify which data is actually needed (data minimisation), whether personal data is involved and whether it can be pseudonymised or removed beforehand. We discuss how your use case is likely to be classified under the EU AI Act and which transparency obligations may apply. The legal assessment is made by you with your data protection officer or lawyer.
Choose model and hosting
Depending on your requirements, we compare OpenAI models, offerings from major cloud providers with data centres in the EU, and self-hosted open-source models. We also review the contract terms, for example whether inputs may be used for training, and prefer options where this can be excluded contractually.
Prototype with real data
Instead of slides, we build a working prototype early on, using a representative sample of your data. A set of test questions or test documents with expected results shows objectively how well the solution performs.
Development in two-week sprints
The solution is built step by step in two-week sprints, with a demo at the end of each sprint. You see continuously what works and can adjust priorities.
Operation, monitoring and handover
Go-live includes logging, cost control, quality monitoring and training for your team. After that you receive three months of free technical support; for ongoing operation we agree an SLA.
AI solutions for mid-sized companies: what we build
- Intelligent document processing
- Invoices, orders, contracts or forms are read, classified and converted into structured data automatically. Uncertain cases go to a person for review before anything is passed into your ERP or accounting system.
- Internal knowledge search (RAG)
- Using retrieval-augmented generation, an assistant answers questions from your own documents and cites its sources, so every answer can be checked. We respect access rights from your existing systems so that nobody sees content not intended for them.
- Assistants for staff and customers
- Assistants for sales, customer service or back office that prepare draft replies, summaries or quotes. The decision stays with a person; the assistant's capabilities and limits are communicated clearly.
- Building data pipelines
- We bring together data from ERP, CRM, databases, files and APIs, clean it and make it ready for analysis. Depending on volume and freshness, as batch jobs with Apache Spark or as a stream with Kafka.
- Reporting and dashboards
- Figures that update themselves from one shared, traceable data source, instead of hand-kept spreadsheets that disagree with each other.
- Putting ML models into production
- We move existing or newly developed models, for example for forecasting or classification, into stable operation: as a service with an API, versioned, monitored and with a traceable process for new model versions.
Technologies
Engagement models and terms
AI projects often start with open questions. That is why we usually begin with a compact requirements analysis and a prototype, and then choose the right engagement model. All quotes are in euro.
- Fixed price after requirements analysis
- Once the use case, data basis and quality criteria are clear, we offer the implementation at a fixed price.
- Time & material
- For exploratory work, where the best approach only becomes clear once we work with the data, we bill transparently for the actual effort.
- Dedicated team
- For longer-term data and AI initiatives, we provide a stable team on a monthly basis that gets to know your systems in depth.
Frequently asked questions about AI integration
Will our data be used to train the AI models?
That depends on the provider and the contract chosen. With some providers, business terms state that inputs are not used for training. We review the terms with you and, where possible, choose an option in which this is excluded contractually. Where that is not sufficient, self-hosted open-source models are an alternative.
Where is the data processed?
That depends on the architecture, which we decide together. For the language models, cloud offerings with EU regions or self-hosted models are possible. The application itself (databases, search index, interfaces) can run in a data centre in Frankfurt am Main or in your own cloud environment. As a principle, only the data actually needed for the task is processed.
What does the EU AI Act mean for our project?
That depends on the specific use case. Internal knowledge search or document processing is typically assessed differently from systems used for recruitment decisions or credit scoring. Early on, we discuss the likely classification and possible requirements with you, such as transparency or documentation, and take them into account technically. We do not provide legal advice; the binding assessment should be made by your legal adviser or data protection officer.
How do you stop the AI from making up wrong answers?
Language models can make mistakes, and this cannot be ruled out entirely. We reduce the risk in several ways: answers are grounded in your documents with sources cited, the assistant says clearly when it finds no suitable information, quality is checked continuously against a set of test questions, and important decisions are confirmed by a person.
Do we need our own data scientists?
No. We bring the necessary expertise in Python, LangChain and Spark. On your side, you need a contact person who knows the processes and data, can answer questions and assess results. After go-live we train your team so it can use and develop the solution with confidence.
When will we see first results?
We work in two-week sprints and show the working state in a demo at the end of each sprint. So results are visible regularly, not only after months. The overall duration depends on the use case and the state of your data; we estimate it after the requirements analysis.
Do you sign a data processing agreement?
Yes. Whenever we process personal data on your behalf, we offer a data processing agreement under Art. 28 GDPR.
Other services
Custom software
Back-office systems, APIs and ERP-style software built around the way you work. C#/.NET, from requirements analysis to operation.
Learn more →Web & mobile development
Web applications, customer portals, admin panels and cross-platform apps built with React, Next.js and Flutter. Fast, accessible and usable on any screen.
Learn more →Cloud & DevOps
Infrastructure as code, CI/CD pipelines, Kubernetes and cloud migration to AWS and Azure. In EU regions such as Frankfurt if you need it.
Learn more →Team extension
Experienced engineers, tech leads or a complete team from Baku join your existing team and work in your tools and processes.
Learn more →White label
We build software for agencies and IT companies under their brand. The client stays yours and our name appears nowhere.
Learn more →IoT solutions
Vessel and equipment management systems: we collect device data in real time, show it on a dashboard and flag problems before they happen.
Learn more →Let's talk about your AI use case
In a free 30-minute call we discuss where AI makes sense in your company, which data it needs and what a first prototype could look like. In German if you prefer. We reply within 24 hours on working days.