AI consulting

AI consulting that changes how your engineers build.

Most teams bought the licences, saw a bump, and levelled off. That is what happens when AI is used to type faster inside a way of working that was designed around typing. We embed with your engineers, in your codebase, until the way they build has actually changed: agentic workflows, frontier models with a real harness around them, and one engineer directing several streams of work at once.

We work in your repository, on your real work, alongside your engineers.

Agentic workflows, frontier models, and the harness that makes them reliable on your stack.

Your team keeps the way of working, not a document describing one.

The plateau

Most teams are using AI to do the old job faster.

The licences went out, everyone got a good autocomplete, and the curve flattened. That gain is real and it is small, because the tool changed and the shape of the work did not. A developer still picks up one piece of work, holds it alone, and types most of it. That is the part worth changing.

  • AI used as a quicker way to write the code someone was going to write anyway.
  • One engineer, one piece of work, carried start to finish mostly by hand.
  • Models driven a few minutes at a time, with someone watching every step.
  • Practice that varies by developer, because none of it is shared or version-controlled.

What changes

The unit of work stops being what one person can type.

An AI-native engineer describes intent, runs several streams of work at once, and spends their own time on the judgment calls and on checking the result. The model does the typing. That is a different job, and it needs a different setup around it: frontier models, a harness that knows your codebase and your conventions, and the discipline to verify properly.

  • Agentic coding workflows, where an engineer directs the work rather than typing it.
  • Frontier models, chosen and kept current, because the ceiling moves every few months.
  • A harness built for your repository: the context, the conventions, the commands, and the checks.
  • Verification as the core skill, because that is where an AI-native engineer's time now goes.

How we do it

We build it with your team, on your real work.

This is not a workshop and it does not arrive as a document. We sit with your engineers and do the work alongside them, in your repository, on things that were already on the roadmap. A way of working only survives if it is built where it will be used.

  • We start on work your team already owed, not on an exercise.
  • The harness is built in your repository and version-controlled like the rest of the code.
  • Engineers learn it by shipping with it, next to someone who already works this way.
  • We leave when your team is running it without us.

Questions

Common buyer questions.

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.