1. Extraction confidence is not business approval
An AI model can be confident that it read a value correctly while the value still violates the business process. A supplier identifier can be well extracted but unknown to the customer. A total can be clearly read but fail arithmetic. A purchase-order number can look plausible but use the wrong format.
That is why MicroExtract treats extraction and validation as separate stages. Extraction answers “what appears to be on the document?” Validation answers “is this output acceptable for this workflow?”
2. Use deterministic rules where the business rule is deterministic
Rules are useful when the acceptance condition can be stated explicitly. Common examples include required fields, identifier patterns, date relationships, arithmetic, allowed currencies, reference-table lookups and cross-field consistency.
subtotal + tax = totalpurchase_order matches ^PO-[0-9]{5}$currency in allowed_currenciessupplier_id exists in supplier_masterThese checks do not replace AI. They provide a controlled layer around the AI output.
3. Failing a rule should create an exception, not a silent guess
A production workflow needs a deliberate path for uncertainty. When a configured rule fails, the record should carry the reason for failure and enough context for a reviewer or downstream process to decide what happens next.
Examples include “invoice total mismatch,” “mandatory PO reference missing” or “supplier not found in reference data.” This is more operationally useful than a single generic confidence score.
4. Design validation around downstream risk
Not every field needs the same level of control. Start with the fields that can cause a payment error, compliance issue, failed integration or material rework. Then decide which checks can be deterministic, which require reference data and which still need human review.
5. Measure extraction and validation separately
When evaluating a document automation implementation, distinguish field extraction quality from end-to-end accepted-output quality. A workflow can improve even when some documents continue to require review, provided the exceptions are correctly identified and the trusted output is delivered reliably.
AI extracts. MicroExtract validates. The objective is not to make uncertainty invisible; it is to prevent uncertain data from silently becoming trusted business data.
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