Your operation is your
training data.

RLHF firms charge $8+ per preference pair. Nebbos generates them as an operational byproduct — every time your team approves an agent’s suggestion, edits it, or overrides it, that’s labeled training signal for your model. No labeling contract. No side workflow. Just the work you were doing anyway.

JSONL preference pairs for RLHF. Chat-format traces for supervised fine-tuning. Structured eval sets sliced by department. Every export carries the audit chain so a compliance question about a training example lands at the specific approval decision it came from.

The training corpus is yours. You own the tenant, the substrate, the data. Fine-tune a Claude, a GPT, a Llama, your own from-scratch model — swap the base without losing what your team taught it. Your moat compounds inside your walls, not somebody else’s API.