AI, RAG, LLM
AI on your own data — practically.
We build AI solutions that use your own corporate data: RAG systems, internal assistants, and decision-support automation. No demo hubris.
Most LLM projects fail because answers are not connected to the company's own knowledge. We build RAG systems that retrieve the right documents before the model answers, and that record the sources.
How we build RAG and LLM systems
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Use case and data
We discuss where AI actually fits and where it does not. Sometimes good search and rule-based logic do more than an LLM.
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RAG architecture
We build the pipeline: document indexing, vector search, context assembly, and the model itself. We use open libraries like LangChain and custom Go/TypeScript implementations where they make sense.
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Safely into production
Audit logs, access control, cost tracking, and a fallback to rule-based behaviour when the model is not reliable. EU sovereignty informs model choice.
Tech stack
- Python
- Go
- TypeScript
- pgvector
- Qdrant
- LangChain
- Azure OpenAI
- Bedrock
Frequently asked
What does a RAG system need in practice?
Indexed documents, vector search, a good prompt, and a model. The hardest work is usually data cleaning and indexing, not picking the model.
Can we keep the data in the EU?
Yes. We use EU-hosted models when needed (Azure OpenAI EU, Mistral, local open models). This is now a requirement for almost all our customers.
What does the EU AI Act mean for an SMB in 2026?
Most SMB use cases fall in the lowest risk class. High-risk applications (recruitment, health, education) require more documentation. We can help work out which class your use case falls into.
AI is a good tool when the use case is precise and the data is in order. We start there.
Get in touch
Start with a calm conversation.
Call, email, or grab 30 minutes from our calendar. We reply within one business day.