ai / ml engineering
applied ai that works in production, from models to monitoring.
We add AI to real products and workflows, and we build it to hold up in production. A demo is the easy part. The harder work is measuring quality, handling the cases a model gets wrong, and knowing when its behaviour changes.
Every AI feature we build comes with a way to evaluate it and monitor it, so you can see how it performs on your own data and decide with evidence whether it is good enough.
what you get
- llm and machine learning features
- data and evaluation pipelines
- monitoring in production
when it fits
- People spend hours on repetitive reading, sorting or data entry.
- You want useful answers from your own documents and data.
- An AI prototype works in a demo but isn't reliable enough to ship.
typical work
document extraction, search over your own data or an assistant inside your product.
common questions
do we need our own data?
Not always. Many features start from existing models, and your data is used to test and improve the result for your case.
how do you know it works?
We agree how quality will be measured first, then test against real examples before and after release.
have something specific in mind?
request a proposal