Introduction

typestrict trains a small decision model on your data: it reads a ticket, a message or a document and answers with a structured, typed decision (a label, a yes or no, a score), the same answer shape as Jev, the market reference model. If your model does not beat Jev on examples it never saw during training, the credit comes back.

How a training goesCopy Anchor Link

Every training goes through five steps, in the dashboard, the API and the assistants alike:

1. Data: you send files, connect Google Drive or OneDrive, or start from a public dataset. Personal data is masked before anything else reads it.
2. Review: our agent organizes the data into training examples. You check the proposal and adjust it.
3. Base model: you pick the model to train on, cleared for commercial use, and confirm the cost.
4. Training: we train it and measure it against Jev.
5. Result: you download the model (ONNX and weights) and use it anywhere, without a GPU.

Where to startCopy Anchor Link