An AI-augmented team designed to get smaller over time.
It starts with six engineers. As they learn your business and turn that knowledge into tools, the work needs fewer people.
What an AI-augmented team is
Many offers called “AI-augmented” are the same engineers you would hire anyway, using an AI assistant.
Ours works differently: the team is designed to get smaller.
A team of around six engineers joins you. In the first weeks they learn your business: what your systems assume, which edge cases actually happen, and what “done” means in your organization. That knowledge is the most expensive part of the work, and an AI tool can’t produce it on its own.
The team then turns that knowledge into working assets: prompts, evaluations, pipelines, and playbooks that record your rules. Once those rules are written down instead of living in someone’s head, the work needs fewer people. The team goes from six to four, and then to two.
The two engineers who stay are the ones who work best with these assets. The others move on to another project with us.
What you get
Measurable efficiency
The cost per unit of work goes down, and you can see where.
AI adoption that sticks
Your team uses AI in its daily work, with tools built around your processes.
A smaller team
The team shrinks as the work needs fewer people, on a plan you can see from the start.
No layoffs on your side
The engineers who leave the team move on to other projects with us.
Before you start
Four conditions this model needs.
The work has to repeat
Repetitive work with a stable definition of done can be handled by fewer people over time. Exploratory, one-off work can’t, so the team stays the same size.
Your rules have to be written down
The assets are built from rules someone can document. If the rules exist only in people’s heads, the team can’t shrink.
Someone on your team owns the assets
The assets are what allow the team to shrink, so someone has to maintain them after we hand them over.
AI usage has a cost
Model usage (tokens) is a new line in your budget. It’s smaller than the cost of the roles it replaces, and it should be in the business case from day one.
The limits we’re clear about
The team is designed to go from six engineers to two, but the final size and the timeline depend on your domain. We can only estimate them after we see the work.
The assets replace repetitive work. Judgment stays with a person: deciding what to build, knowing which edge case could cost you a customer, and being accountable when something breaks at 3 a.m.
Ask how this would work for your teamWhen it doesn’t go to plan
Three ways this model can fail, and what we do about each one.
We tell you in the first month
If every task turns out to be a different problem and the work doesn’t build on itself, we recommend switching to traditional staffing.
We tell you when progress stalls
The assets can only capture what someone is willing to write down. If the real decision rules live with one senior engineer who has no time to share them, we tell you right away so the project keeps its direction.
We name an owner before the handover
The assets need maintenance, like any other code. After six months without it, the team starts growing again. Before we hand them over, we recommend who on your team should own them.
Why not build your own framework?
You can build your own. Ours is ready on day one.
Our engineers arrive with the frameworks and playbooks of the ACL group, developed across 30+ years and 7,000+ hires. Backed by ACL & DataArt.
That saves time at the start. Your engineers don’t have to build review, access, and data practices from scratch, so the first weeks go into learning your systems and your business.
30+
years
7,000+
engineers hired
200+
companies
25
technical recruiters
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