Evaluate AI to read PDF orders: fields, quality, trust, review and integration with ERP before automating commercial entry.
Index
A PDF can contain a table, observation, discount and address in variable positions. Extracting text is different from understanding the request. Hiring AI must start with the fields that need to reach the system and the conditions that require a review before creating any operation.
Decision this guide helps you make: Convert received documents into verifiable orders, without accepting extractions by appearance.
Create a sample that represents receipts
Include vendors with different layouts, poor scans, and documents longer than one page. Use authorized files and protect sensitive information. Separate the development sample from the final evaluation to avoid testing only known examples. Record the correct result before comparing tools.
Validate relationships beyond isolated fields
Quantity, unit, code and price must remain linked to the right item. Totals can help detect inconsistency, but they do not prove that all fields are correct. Define validation against catalog and registration. An unknown code should generate a pending issue, not an automatic creation based on an uncertain interpretation.
Design an efficient review
Show the operator the extracted field and the source snippet. Highlight relevant questions without requiring a complete re-reading of simple documents. The confidence level reported by the model should not be treated as a guarantee. Combine deterministic rules and human review according to the impact of the error, especially before recording values and quantities in the ERP.
Compare cost per completed order
Include processing, storing, reviewing, and forwarding in addition to model calling. Measure conference time and corrections by document type. A cheap per page solution can be expensive if almost everything requires rework. The pilot must also demonstrate the failure path and prevention of duplicate orders.
- Diverse sample with expected results.
- Link between fields and evidence in the file.
- Approval before recording sensitive operations.
A scenario to check out in the demo
Hypothetical example: the PDF presents quantities in boxes, while the catalog uses units. Extraction can get the number right and still produce a wrong order. The flow needs to recognize the unit and apply a validated conversion or request review. Include this case in the acceptance sample to assess operational understanding, not just the ability to read characters.
Briefing to request a proposal
- Required fields and expected units in the target order.
- Difficult documents and correct results defined in advance.
- Conditions that prevent creating the order without approval.
Start with a document flow
Quantum9 can evaluate the reading of a family of orders and their connection with the ERP. The initial commitment must be measurable proof of extraction and verification, before promising widespread automation of every document received.
Discover the scope of AI and process automation and deepen the context in related guide.