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Most AI projects stop as a demo that impressed a meeting and never went live. We build the other kind: agents and LLM features that stand in your workflow, on your own data, with operations afterwards.
See what we buildSix things people actually ask for. A project is rarely only one of them, and never all six.
An agent that finishes a piece of work rather than just answering: fetches the data, makes a decision, writes back into the system. We run them self-hosted on Hermes, with tool access over MCP and a log of what the agent actually did.
Search and answers based on your documents, contracts and handbooks, with the source named in the answer so somebody can check it. Hybrid search with BM25 and embeddings, and a rerank model over the hits before the model gets to see them.
What is copy, paste and manual judgement today. Form in, right place out, with an alert when something has to be looked at by a human.
AI as a feature in your own product: summarising, filling in, classifying. Built so that the cost per user is known.
PDFs and images in, structured fields out. Extraction that can be checked against the original instead of taken on faith.
A test set that catches quality dropping when a model or a prompt changes, and a ceiling on what a run is allowed to cost.
From idea to production. Four steps, and we do not disappear after the third.
We go deep into your problem before writing a single line of code.
Architecture, UX and data flow. Planned before anything at all is built.
We work fast without taking shortcuts. Clean code, tested, deployed.
We stay on after launch. Adjusting, fixing and scaling.
Want to see the craft before you buy it? Ten pages built with no brief and no deadline, source open. See the lab →
What we get asked in the first conversation, answered before you have to ask.
It depends on scope, and we give an estimate after a conversation about what needs solving. A bounded integration is days. An agent working on your own data is weeks.
Claude from Anthropic for what needs the most reasoning, Hermes from Nous Research for agents we run ourselves, and open-weight models like Mistral, Qwen and DeepSeek where everything has to stay on your own machine. For search, Nomic comes in on top for embedding and reranking. The model is chosen to fit the task, and we lock no solution to a single vendor, because price and quality move fast.
Yes, that is the whole point. The data stays with you, and we use vendor settings where it is not used to train models.
A data processing agreement, an EU or EEA region where the vendor offers one, and we do not send more personal data into the model than the task requires. Where the requirements are stricter we can run an open-weight model self-hosted, so that no data leaves the machine.
It is going to answer wrong. That is why we build with source citation, test sets and a human in the loop on anything with consequences.
We stay on. Models change, data changes, and an agent nobody maintains gets worse over time without anyone being told.
