AI Document Processing: Turn PDFs and Emails into Checked Records

AI document processing extracts useful information from documents and prepares it for another system. A purchase order might become a draft order record. A completed service form might become a job request. The value comes from reducing repeated reading and retyping while keeping the result traceable to its source.

Extraction is only one part of the workflow. A system also needs to recognise the document, check the extracted fields, handle uncertainty and confirm that the destination accepted the record. Without those steps, faster data entry can create faster mistakes.

Start with one document family

Choose a narrow first use case, such as supplier delivery notes arriving in one shared mailbox. Collect examples that represent the actual variation: scanned pages, digital PDFs, different layouts, missing fields and multi-page files.

Define the fields the next process needs. For a delivery note, that might include the supplier, delivery reference, related order number, items and quantities. Specify whether each field is required, where it comes from and how it should be represented.

Avoid starting with “read every attachment”. An email signature image, a terms document and a delivery note should not all enter the same extraction path.

Design the complete processing flow

A practical sequence is:

1. Receive the file and record its source event.
2. Identify the document type and check whether it is supported.
3. Extract candidate fields, retaining references to the source content where available.
4. Validate required fields and compare them with existing business records.
5. Route uncertain or inconsistent results to a reviewer.
6. Create or update the destination record and capture its identifier.

Store a processing state for each document. “Received”, “awaiting review”, “ready to transfer” and “transferred” mean different things. A successful extraction does not mean the order or job record has been created.

Separate confidence from correctness

Some extraction systems return confidence scores alongside their predictions. These can help prioritise review, but the score is not a substitute for checking the business meaning of a field. Microsoft’s Document Intelligence guidance explains confidence interpretation and using representative samples to assess results.

For example, a clearly printed order number can be extracted confidently but refer to an order that does not exist in your system. A quantity may be readable yet conflict with the remaining quantity on the order. These cases need business validation, not a different confidence threshold.

Set review rules at field level where possible. An optional note can tolerate a different process from an identifier that determines which customer record is updated. Choose thresholds from measured examples; do not assume one percentage suits every document and field.

Give reviewers enough context to decide

Show the original document beside the proposed values. Highlight missing fields and explain which check failed. Let reviewers correct a value, reject the document or request more information.

Record the correction and the reason. That helps distinguish poor source quality from an extraction problem or an outdated matching rule. It also gives you useful examples for the next evaluation round.

An empty field should remain empty until resolved. Asking an AI model to fill every field can encourage plausible guesses. The workflow needs a supported way to say “not found”.

Example: delivery notes into a job system

Imagine a hypothetical installation company receiving delivery notes from several suppliers. The workflow identifies the document, extracts the order reference and prepares a list of delivered items. It checks the reference against open jobs before preparing an update.

If the reference matches two jobs, the record waits for review. If the same attachment arrives twice, the second event is linked to the existing processing record. If the job system is unavailable, the checked data stays queued instead of being marked complete.

The first pilot could create draft records only. Automatic updates can be considered later for well-defined cases that pass the agreed checks. This example describes a possible implementation, not an existing client result.

Test failures as carefully as clean documents

Test rotated scans, unreadable pages, duplicate files, revised documents and unavailable destinations. Distinguish a repeated copy from a genuinely revised version; a file hash alone will not identify every business duplicate.

Also test content that looks like an instruction to the automation. A document is input data. It should not be able to change the destination, reveal other records or authorise an unrelated action.

Measure field correctness, review time, correction frequency and successful transfers. A high extraction rate is not enough if staff spend longer checking results than they previously spent entering them.

Frequently asked questions

Is this the same as OCR?

OCR converts text in an image into machine-readable text. A document workflow goes further by identifying relevant fields, validating them and moving checked information into the next process.

Can we remove human review completely?

Some narrow cases may qualify for automatic processing after evaluation. Keep a review path for unsupported documents, uncertain matches and exceptions, with requirements based on the consequences of an incorrect update.

What should we prepare for a pilot?

Bring anonymised sample documents, required fields, common errors and details of the destination system. Use our automation audit guide, explore AI automation at Kreatrs, or discuss a document workflow.