CAPE is a methodology for training custom capabilities into language models via a process we call Capability Engineering.
Capability Engineering is the natural evolution of efforts to make AI useful in practice: Prompt engineering tells the model what to do. Context engineering gives it the information it needs. Capability engineering ensures it actually does what you want, by default.
CAPE operates across the entire model lifecycle: at inference to verify compliance and generate training signals, during training to embed capabilities reliably into the model, and for evaluation to confirm the model meets your requirements.
There's a gap between what AI labs ship and what enterprises need—between what a model can do and what it reliably does in production. We call this the deployment gap. CAPE closes it.
Conventional wisdom held that many requirements are too subjective to specify or train for. Our research shows this is only true at the general level. Once you fix the context, the subjective becomes objective—and therefore specifiable, measurable, and trainable. This opens the door to user-side post-training that simply wasn't possible before.
We work with frontier labs, regulated enterprises, and AI-native companies to engineer economically valuable capability. Contact our team to get started.