Build it where it has to run, or you are guessing.
A model that works on your laptop and a model that works inside a bank are not the same model. The gap between them is the whole job. Forward-deployed means you close that gap where it actually lives: in the client's environment, against the client's data, under the client's constraints.
Why we ship inside the walls
Most AI work happens at a safe distance from the place it is supposed to help. Someone builds against a clean export, demos it, and hands over a slide deck and a repo that has never met the production network. Then the client's team spends six months discovering everything the demo quietly assumed.
We do the opposite. The FDE builds inside the client's environment from week one: their cloud, their data boundaries, their compliance reality. The friction you hit in week one is the friction that would have killed the project in month six. Meeting it early is not a tax. It is the point.
Build-and-transfer, not build-and-leave
There are two failure modes in applied AI, and they are mirror images. A consultancy builds you something only it can run, then bills forever to keep it alive: that is build-and-leave, dressed up as partnership. A school teaches you theory you cannot apply to your actual systems: that is the opposite distance problem. Both leave the client dependent, just on different things.
Cohorte does both halves and bridges them. We teach what we ship, and the system does not leave with us. The FDE ships it; a Principal Mentor and the client's own operators take ownership. When we walk out, the thing keeps running, and the people running it understand why.
What we are in the business of
We are in the business of systems that hold up and humans who can run them. AI is only as good as the human operating it, so a system nobody on the client's side can operate is not an asset, it is a liability with good PR. Verification is the spine of this: a system that cannot say "I do not know" has no business near a credit decision or a patient record. You build that refusal in, and you prove it works, because plausible and correct are not the same thing.
What we ask of you
Three things, and they are non-negotiable.
- You have shipped into real stakes. Not advised on shipping. Personally put AI systems into places where a mistake mattered, and lived with what happened next.
- You build for the handover. You write the system, the runbooks, and the monitoring so someone else can own it. The measure is whether they can run it without you, not how clever it was.
- You take verification seriously. You make the system abstain when it should, and you measure that it actually does. Warm toward the client, unsparing toward plausible bullshit.
The one line to carry. The client does not remember the demo. They remember whether the system was still running, and still trusted, a year later, and whether their own people could keep it that way. Build for that, and the engagement works. Build for the applause in the room, and no architecture can save it.
If that sounds like you
Read what the role actually involves, then the bar we screen against. If you still recognise yourself, apply.