AI-powered sales orders: from customer document to a real ERP operation

A purchase order arriving by email may contain a customer reference, multiple lines, delivery addresses, dates, dimensions and descriptions that do not match the ERP catalogue exactly. Reading those values is useful, but it is not yet a sales order.

The system must understand whether the message creates a new order or requests an action on an existing one. It must resolve the customer, prevent duplicates and preserve the original evidence.

A model can propose structure and intent, while deterministic rules validate customer-specific references, products, formats, units, dates and delivery splitting. This separation makes the result explainable and testable.

When certainty is insufficient, the correct result is a visible exception with enough context for a person to decide—not a plausible invention.

The resulting order retains the relationship between document, interpretation, validation and ERP record. Idempotent integration, auditability and controlled retries matter as much as extraction quality.

The goal is not to remove people from the process. It is to reserve their time for exceptions while routine, well-defined work becomes faster and more consistent.