There's manual work trapped inside your documents and processes.
An AI demo takes an afternoon and looks like magic. Getting it into production is another matter: it's right 90% of the time, and the other 10% it's wrong with total confidence. All the engineering lives in that 10% — spotting it, containing it, and deciding what happens when it occurs.
That's why we don't ship anything without a way to measure whether it works, and without a person in the loop wherever being wrong costs money. AI that isn't evaluated isn't a product, it's a bet.
First we agree how we'll know this works. Without that there's no way to tell a system that's right from one that merely sounds right.
RAG over your documentation, your processes and your vocabulary. The value isn't in the model, which is the same one everyone else has: it's in what it knows about you.
Clear-cut cases go through on their own; the doubtful ones reach whoever decides, already prioritised. The team reviews instead of typing.
Our own product, in production. Download it and judge for yourself.
Giving AI the Spanish met office's forecasts. An open source MCP server, on npm and in production.
If you want to «add AI» without a specific process to fix. That's a solution looking for a problem, and it shows up on the invoice.
If you need it right 100% of the time with nobody checking anything. That doesn't exist today, whoever tells you otherwise.
If your data is so disorganised that even your team can't find it. AI doesn't fix that; it amplifies it.
If you recognise yourself here, tell us anyway. I'd rather point you somewhere else in an email than have us both find out halfway through the project.