AI, used sparingly and only where it pays.

Where a model earns its place

Language models are very good at a specific shape of problem: input that is messy, varied and written by humans, where the rules are real but there are hundreds of them and they all have exceptions. Classifying free-text notes, pulling structured fields out of documents that were never designed to be parsed, summarising a queue nobody has time to read.

Those are the jobs where writing the logic by hand means writing it forever, because every new input finds a case you didn't think of.

Where it doesn't

If the rule is a rule, write the rule. A model that decides whether a number is greater than another number is slower, more expensive, occasionally wrong, and impossible to test properly. Arithmetic, validation, routing on a known field, anything with a correct answer you could look up: all of that is code.

We've built language technology the hard way, writing the logic by hand to catch a grammar mistake in Swedish, and prepared the data that machine learning systems run on. That's how we know the difference between a model earning its keep and a model being an expensive way to avoid writing code.

Built so you can check it

Anything a model decides gets logged with what it saw and what it produced, so a wrong answer is something you can go and look at rather than something you argue about. Where the cost of being wrong is high, a person confirms before anything is committed.

We'll also tell you what it costs to run, per document or per month, before you commit to it. That number decides more projects than the technology does.