We sit with the process. What happens today, who touches it, where it breaks, what the data actually looks like.
The architecture: which models, what retrieval, which integrations, where the human stays in the loop.
Building the agents, pipelines, APIs, and interfaces. Working demo at the end of every sprint.
Wiring it into the tools you already run, so nothing needs copying between tabs.
Run it against real historical cases. Report accuracy, failure modes, and cost per run before launch.
Ship it, monitor it, and set up the evaluation loop so it improves rather than drifts.
Process first, model second.
The model is the easy part. Most failed AI projects failed at the process, not the prompt.
Show the failures.
Every system we hand over comes with an honest accuracy report, including where it gets things wrong.
Built to be handed over.
Documented, readable, and yours. No dependency on us by design.