In many offices, someone still spends most of the day moving numbers from paper into a system. Supplier invoices are retyped into the accounting software, expense receipts are logged one by one in a spreadsheet, and customer registration forms are copied into the CRM. The work is tedious, and a small mistake such as two swapped digits may only surface at month-end closing.
Automating data entry with AI and OCR offers a realistic way out. The machine reads the document and fills in the fields, and a person checks anything that looks uncertain. This article explains how it works, which documents are good candidates, how to build validation in, and how to get started.
How OCR and AI work together
OCR (optical character recognition) turns images and scanned PDFs into text. The technology is mature and reliable for clean printed documents. With skewed or dark phone photos, results can be much worse. Its limitation is that it only produces raw text. OCR has no idea that the figure in the bottom right corner is the invoice total, or that the string of digits at the top left is the invoice number.
That is where a large language model (LLM) comes in. Once the text is extracted, the AI reads it in context and maps the content to the fields you define, such as supplier name, date, invoice number, line items, VAT and total. Some models can also read images directly, so text recognition and understanding happen in a single step.
The result is structured data, usually JSON, ready to be sent to your accounting software, ERP or internal application through an API.
Documents suited to automated data entry
Supplier invoices
This is the most common starting point. A distributor or building supplies store that receives dozens of invoices a week from many suppliers can extract the header data and line items, then match them against purchase orders before anything enters the system.
Receipts and expense claims
Service companies with many field staff often struggle with reimbursement claims. Staff photograph the receipt, and the system reads the date, merchant and amount. Plan for poor photos and faded thermal paper, since both tend to reduce accuracy.
Forms
Patient registration forms at a clinic, order forms and field survey forms are often filled in by hand. AI can help, but accuracy on handwriting varies more than on printed text. Expect a larger share of human review for this type of document.
Identity documents such as KTP
Reading an Indonesian ID card to fill in details for a new customer or employee is technically simple. The challenge is personal data. NIK numbers, addresses and dates of birth are personal data protected under UU PDP. Make sure you have a clear basis for processing them, limit who can see the results, and consider masking the NIK before sending data to a third-party AI service when the task does not need the full number.
Validation and human review
AI can misread a document, and its mistakes look convincing. A well-built system therefore does not save every extraction result straight away. It needs two layers of protection.
The first layer is automatic rules. Line items must add up to the subtotal, subtotal plus tax must equal the total, and dates must be valid. NPWP and NIK numbers have a fixed number of digits, and the same invoice number from the same supplier is a likely duplicate. Rules like these are cheap to build and catch a large share of errors.
The second layer is a review queue. Documents that fail validation, or that the AI flags as uncertain, go to a review screen. Staff see the original image next to the extracted fields and only correct what is wrong. Their role shifts from retyping to checking, which is much faster.
Keep a record of every correction. It shows which document types or fields fail most often, so you can improve the rules and the AI instructions over time.
How to get started
Document automation projects most often stall because the scope is too broad from day one. The order below keeps things manageable.
- Pick one document type with high volume and a fairly consistent layout, such as invoices from your five largest suppliers.
- Collect real samples, including blurry, skewed and folded ones.
- Define the required fields and their output format.
- Write the validation rules together with the finance or admin staff who do this work today.
- Test on past documents with known correct values, and measure which fields fail most often.
- Run it alongside the manual process for a few weeks before retiring the old way.
- Connect the output to the target system through an API so nobody has to copy and paste.
Starting small tells you quickly whether the automation is worth it, without putting your whole finance process at risk.
Document extraction for your systems
Averoa builds document data extraction that reads invoices, contracts and forms and turns them into structured data, with validation rules and testing on real documents. When the output needs to reach your accounting software or ERP, we also handle system integration. The full details are on our AI integration page.
