Document processing automation
We build document pipelines that read incoming files, extract the fields you need, check them against your rules and send anything uncertain to a person before it reaches your systems. Every extracted value keeps a link back to its source.
Extracted fields
| Field | Value | Check |
|---|---|---|
| Supplier | Northfield Packaging Co. | Passed Matched to supplier record |
| Invoice number | INV-20418 | Passed Not seen before: no duplicate |
| Invoice date | October 12, 2026 | Passed Within the accepted range |
| PO number | PO-7731 | Passed Matched to an open purchase order |
| Total | $4,815.00 | Passed Line items add up to the total |
| Payment terms | Net 45? | Review Low confidence: text partly obscured |
Sent to the accounts payable queue. A person confirms the terms against the PDF.
The problem
Every new customer, supplier or applicant adds documents someone has to read and key in.
A mistyped amount or date is easy to make and slow to trace once it is in your system.
The same type of document arrives in dozens of formats, scans and photos.
When a number is questioned, finding the original page takes longer than the original entry did.
Scope and deliverables
Supported inputs depend on scope and samples; we confirm what is practical after reviewing your documents.
Representative documents, including messy scans, to judge what can be extracted reliably.
Which fields matter, their formats and the validation rules they must pass.
Text recognition for scans where needed, and extraction tuned to your document types.
Totals that add up, dates in range, matches against existing records and duplicate detection.
Uncertain fields shown beside the source page, so a person can confirm or correct them quickly.
Confirmed data written to your accounting, CRM or operations system, with a link to the source file.
Example scenarios
Fictional examples. They describe typical document types, not Alaysalee clients or measured results.
Situation
An accounts team keys 300 invoices a month from email attachments.
What would be built
Invoices are extracted, matched to purchase orders and checked for duplicates, then prepared as bills in the accounting system.
A person stays involved
Mismatches, new suppliers and low-confidence fields go to a reviewer before anything is posted.
Situation
A staffing agency receives completed onboarding packets as PDFs and photos.
What would be built
Contact details, start dates and checklist items are extracted into the onboarding record.
A person stays involved
Identity documents are not processed automatically and stay with authorized staff.
Situation
A property manager reconciles monthly statements from several vendors.
What would be built
Statement lines are extracted into a standard table and compared with expected charges.
A person stays involved
Any unexpected charge is flagged for a person to investigate.
Trust, security and oversight
Document processing is never 100% accurate, so the design assumes some fields will need a person.
Fields below the agreed confidence, or failing a rule, are held for review rather than written.
Every confirmed value links to the page it came from.
Only authorized staff can see documents and the review screen.
How long originals and extracted data are kept, and where, is agreed before real documents are processed.
Unreadable, incomplete or out-of-scope documents are returned to a queue with the reason.
Capabilities and limits
Readiness
Fit
Engagement approach
Scope depends on the number of document types, their variability and the destination system.
We study representative documents, including messy scans, to judge what is reliable.
We define the fields, formats and validation rules each document must pass.
OCR and extraction are tuned and combined with confidence and duplicate checks.
Uncertain fields appear beside the source page for a person to confirm.
Confirmed data goes to your systems with a link to the original document.
Many scans can be processed with text recognition, but quality matters. Faint, skewed or handwritten documents produce more low-confidence fields, which go to review. Your samples show what to expect.
Variable layouts are common. Modern extraction handles many variations, and validation rules catch mistakes. Very unusual layouts may always need review.
We measure accuracy per field on a test set before launch and monitor the share of documents that need review afterwards. Corrections made by reviewers help identify where rules or instructions should change.
It depends on how long each document takes by hand and how costly errors are. We help estimate the trade-off during discovery rather than assuming automation always pays off.
Systems with an API or import format, such as many accounting, CRM and operations tools. We confirm the destination during discovery.
Next step
Tell us the document types, rough volume and where the data needs to go. We will look at whether extraction with review would genuinely save time.
We reply by email. An inquiry is not a contract, and there is no obligation.