Generative AI development
We do generative AI consulting and development, building features that answer from your data, find the right information and connect models to your systems. Every build comes with evaluation, monitoring and clear data handling, so quality stays measurable after launch.
Generative AI services
- Custom RAGChatbots and search that answer from your documents, with citations and permissions.
- LLM evalsTest sets, regression gates and monitoring that show whether your AI is getting better.
- Semantic searchHybrid search with embeddings for products and knowledge, tuned on real queries.
- Custom MCP serversSecure MCP servers that give agents scoped, audited access to internal systems.
How we work on it
Retrieval before generation
Most quality problems start with the wrong context, so we measure what gets retrieved before we tune how the model writes.
Measured on your data
Models, embeddings and prompts are chosen with eval sets built from your content and your users' questions.
Permissions and residency by design
Access rules, redaction and EU hosting are part of the architecture from the first sprint, ready for your security review.
No lock-in to one provider
We keep models swappable behind clear interfaces, so you can move to a better, cheaper or EU-hosted model when it makes sense.
Related work
- Legal
Custom RAG chatbot that answers from a law firm's own precedents, 3× faster
3×faster answers from firm knowledge- RAG
- LangChain
- Hybrid search
- Retail
Semantic product search with embeddings, lifting search-to-cart conversion 24%
+24%search-to-cart conversion- Embeddings
- Semantic search
- Vector database
- Insurance
How LLM evals let an insurance claims assistant ship weekly at 94% accuracy
94%answer accuracy, tested on every release- LLM evals
- Langfuse
- Promptfoo
Built with
All technologiesFurther reading
How to build a RAG chatbot people can trust: a practical guide
What it takes to turn a promising RAG experiment into a chatbot people trust: ingestion, hybrid search, citations, permissions and evals.
6 min read
RAG vs fine-tuning: which one do you need?
RAG gives a model your knowledge at answer time; fine-tuning shapes its behaviour. How to choose, when to combine them and what each costs.
4 min read
LLM evals: testing AI before every release
A practical approach to LLM evals: build a test set from real cases, combine code checks with model grading, and block releases that regress.
5 min read
Common questions
Systems your team uses every day, built on your own data: RAG chatbots and assistants, semantic search, document parsing and extraction, evaluation setups and MCP servers. We build them into existing products or as standalone applications.
For most business use cases RAG comes first, because it keeps answers tied to current documents and shows sources. Fine-tuning helps with consistent formats, specialised language or cheaper inference at scale, and it works well in combination with RAG.
Yes. We deploy models such as Llama and Mistral on your cloud or hardware when data cannot leave your environment, and use evals to check the quality trade-off against commercial models.
We document data flows, minimise and redact personal data, host in the EU where required and keep logs that support transparency and oversight. We help you classify the system's risk level and prepare the documentation your compliance team needs.
We can run and improve it for you under a support agreement, or hand it over to your team with documentation, eval suites and monitoring in place. You can also start with us running it and take it over later.