AI implementation
We’ll be straight about this one, because most of the market isn’t.
AI is genuinely good at a narrow set of things: reading documents and pulling out structured fields, sorting incoming work into the right queue, answering questions from a pile of your own files with a source attached, drafting routine correspondence, and flagging the records in a dataset that look wrong. On those tasks it works, and it works now.
It’s less good at anything where being confidently wrong 4% of the time is a problem you can’t design around.
A lot of pilots stall at that point.
So we start with one task. We measure how long it takes today and how often it goes wrong today, build a narrow version, and run it against your real examples rather than a demo dataset. Then you get a number, and the number decides. If it doesn’t beat what you’re already doing, we say so and stop, and you’ve spent a small amount of money finding something out.
Anything that does go into production ships with the unglamorous parts: logging, a way for someone to review what it produced, a fallback to a person when confidence is low, and a written note on what data goes where and to which provider.