AI engineering

The problem

AI feels like something you should be doing, yet it is hard to see where to begin. Meanwhile hours go to work that wears you down: reading long documents to find one answer, typing up meetings, turning slides into summaries.

What you get

The repetitive part is done for you. Documents and presentations come back as short summaries, recordings come back as text, and routine customer questions get an answer. The decisions stay yours; AI does the reading and the typing. We don’t train models of our own. We take existing ones and build everything around them.

Which tasks suit AI

Not every task suits AI. The best fit is work that repeats in the same way: the same kind of document every week, the same questions from customers, the same type of recording to type up.

The plan of action

First we look for the task where AI makes the biggest difference for you. A scope sets out what gets built, what it costs and what is left for later. We build in short steps and check the results on your own documents and recordings before you depend on them. Depending on its size, a project takes from a few weeks to a few months. Once it is live, we keep it working as the AI services underneath it change.

Without giving up control

AI can be wrong, and that is a fair worry. That is why nothing goes live untested: what we build goes through tests and human review, and a testing phase comes first. Anything that comes up there gets adjusted.

Built before

We built a service that ties several AI services into one pipeline. Documents and slide decks go in and summaries come out; video goes in and comes back as a transcript. We didn’t build the AI models. We built everything around them.

Who you work with