What does AI consulting actually involve?
We embed with your engineers and change how they build, working on your real code rather than an exercise. That means agentic workflows in place of hand-typed changes, frontier models with a harness built around your repository, and enough verification discipline to trust what comes out. What you have at the end is a way of working your team owns.
Our engineers already use AI. What would change?
Usually the ceiling. Most teams use AI as a faster way to write the code they were already going to write, which helps and then runs out. The change worth making is to the shape of the work: one engineer directing several streams at once, verifying rather than typing, with a harness carrying your codebase's context so the model is not guessing at your conventions.
Is this a training course?
No. Nobody has ever changed how they build from a slide. We work next to your engineers on real work until the new way is simply the way they work, and the configuration behind it stays in your repository so it survives us and keeps improving after we go.
How do you know it worked?
By how much gets from started to shipped, and how long it sits in between. We capture that before the work starts so there is something to compare against. Token counts and lines of code are not the measure, and teams that manage to those numbers get worse.