What is training signal?

Training signal is any recorded outcome that teaches a model what good looks like: an approved claim, an accepted draft, a human correction. Products that capture outcomes systematically turn daily operations into a growing dataset, so their models improve with use while competitors' rented models stay static.

The compounding loop is simple: agents do work, humans confirm or correct it, outcomes bind to the inputs that produced them, and the next fine-tune learns from all of it. Accuracy becomes a function of volume.

RecursiveDB is Allocate’s implementation of this loop: every completed task becomes signal, and scheduled training runs turn signal into sharper model versions automatically.

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