Your engineers, shipping it themselves
Leave your team able to build the next one without us
AI enablement is our engineers working inside your team on real production work, so your people can build and run the next system without us. Not a training course. A first system shipped together, with the patterns, the review standards and the evaluation harness left behind in your repository.
- Who it is for
- Engineering leaders whose team needs to own this, not outsource it
- Bench
- 115+ engineers, averaging 15 years each
The usual version of this is a two-day workshop, a slide deck, and a team that goes back to being stuck on Wednesday. The version that works is building something real together, in your codebase, with your constraints, and handing over a pattern the next person can follow.
How it works
- Our engineers embed in your team, in your repository, on work that was going to be built anyway
- The first system ships jointly, with your people writing a real share of it
- Patterns, project structure and review standards land in your codebase, not in a document
- An evaluation harness your team runs, so quality survives our leaving
- Written decisions on what was rejected and why, because that is the part a handover normally loses
- A taper rather than a cliff: we take less of the second system than the first
Work we have published on this
AI systems · Our own product
Fabric
Your project's quality shouldn't depend on which engineer is on it. Our engineers work inside Fabric, an AI system with more than fifty agents that remember each client project's context, and across our clients it's why delivery work that took a team of ten now takes three.
B2B ecommerce
DealerIQ
TotalLoop finds a dealership's customers with equity in their vehicle and matches them to cars on the lot. DealerIQ brought the idea and the dealership know-how, and our senior engineers worked inside its team to build it on Azure, test it with one dealer account and ship a lean first version. Later we moved it off Cosmos DB and SQL Server onto Databricks Lakebase, in waves that could each be rolled back.
FAQ
AI enablement: common questions
What is AI enablement?
Getting a team to the point where it can build and run AI systems without outside help. We do it by building the first one together on real production work rather than by running a course, because the questions that matter only show up against a real codebase and a real deadline.
Is this training?
No, and the difference matters commercially. Training bills for days and leaves slides. This bills for engineering and leaves a system in production plus the patterns to build the next one. Where a team wants formal sessions as well, we run them alongside the build.
How long does it take before our team is on its own?
Usually one system to build together and a second where we take much less of it. That tends to be a few months rather than a few weeks, and it depends far more on how much time your engineers actually get than on anything we do.
What if we would rather you just built it?
Then we will build it, and that is a normal way to work with us. Enablement is a preference some clients have and not a condition. Plenty of engagements start with us delivering and turn into enablement later, or never do.
The rest of our AI work
AI Readiness Assessment
Leaders with AI on the roadmap and no clear view of what is in the way
AI Tools Assessment
Finance and platform leaders holding a renewal they cannot justify
Agentic AI development
Engineering leaders whose agent pilot cannot get past review
AI assistants and enterprise search
IT and data leaders asked for a company assistant that can't leak
AI workflow automation
Operations and engineering leaders with a process that half-completes
AI transformation
Executives with a mandate, a budget, and fifteen pilots