There are two default ways to build AI capability, and both have a predictable failure mode.
Outsource it, and you get velocity without ownership: a vendor ships something, then leaves, and the knowledge leaves with them. Build it fully in-house, and you get ownership without velocity: a capable team learns slowly, hampered by everything they haven't done before.
There is a third model, and it's the one we run.
Embedded, not adjacent
Senior practitioners work inside your team — your systems, your sprints, your channels — not as a separate workstream you manage at arm's length. The line between “us” and “you” goes blurry by design.
You keep ownership. We take accountability. The patterns we bring stay in the building after we rotate off.
Ownership vs accountability
This distinction is the whole model. The client owns the product, the roadmap, and the people decisions. We're accountable for the outcome we were brought in to deliver — and for leaving the team more capable than we found it.
In practice that means transferring patterns as we go: an evaluation harness, a prompt and component library, the unglamorous tooling that turns one good result into a repeatable one. The measure of success isn't how indispensable we become. It's how quickly we're not needed.