The AI-augmented team that’s supposed to get smaller.
Six engineers, a learning curve, and a deliberate plan to need fewer of them.
What an AI-augmented team actually is
Most AI-augmented teams on offer mean the same engineers you’d get anyway, with a chatbot open on the second monitor.
This is different, and it’s uncomfortable to sell: the team is designed to shrink.
A squad comes in — say six people. The first weeks are a learning curve: not learning to code, learning your business. What your systems assume. Which edge cases actually happen. What “done” means in your organisation. That context is the expensive part, and it can’t be prompted into existence.
As that context turns into artefacts — prompts, evaluations, pipelines, playbooks that hold your rules instead of holding them in someone’s head — the work stops needing six people. It goes to four. Then to two.
The two who stay are the ones who best operated the new way. The others move on to the next engagement.
What it checks off
Efficiency you can show
Cost per unit of work goes down and you can point at where.
Real AI adoption
Not a licence nobody uses.
A smaller team
Reached by the work needing fewer people — not by a decision to cut.
Nothing depends on firing anyone
The curve happens on our side of the engagement.
What has to be true for this to work
We’d rather tell you now than at month four.
The work has to compound
Repetitive work with a stable definition of done shrinks. One-off exploratory work doesn’t — six people stay six.
Context has to leave people’s heads
If the rules can’t be written down, there’s nothing to build the artefacts on.
Someone on your side owns the artefacts
They’re why the team can shrink.
There’s a token cost, and it’s not zero
Smaller than the headcount it replaces, but a new line in your budget. It belongs in the business case from day one.
What we don’t promise
We don’t promise six-to-two, and we don’t promise a date. The shape is real; the numbers depend on your domain. Anyone who quotes you the curve before seeing the work is selling you the slide, not the service.
We don’t promise the artefacts replace the people. They replace the repetition. The judgement — what to build, which edge case costs you a customer, who’s accountable at 3am — stays with a person.
Know moreWhen it doesn’t go to plan
The three ways this model fails are all visible early, and here is what we do about each one.
We call it in the first month
If the work turns out not to compound — every task its own problem, nothing accumulating — the honest move is to convert the engagement to traditional staffing, not to keep going.
We tell you when the curve stalls
The artefacts can only encode what someone is willing to write down. If the real criteria live in one senior engineer’s judgement and that person has no time, we say so rather than let it drift.
We name the owner before we hand over
Artefacts decay like any other code: six months without maintenance and the team quietly grows back. We’ll say who we think should own them on your side.
Why us and not the framework you could build yourself
You could. It would cost you the first two months of a team that doesn’t know your business yet.
Our engineers arrive framework-equipped: the group’s frameworks and playbooks come with them, developed across 30+ years and 7,000+ hires. Backed by ACL & DataArt.
That buys you speed: your team doesn’t spend the first two months rebuilding what already exists, and the first pull request looks like the tenth.
30+
years
7,000+
engineers hired
200+
companies
25
technical recruiters
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Tell us which position you can’t fill.
We’ll tell you who can fill it, how long it takes, and what stays on your side.
