Read invoices and documents automatically

AI document processing turns documents into data your systems can use. It reads a supplier invoice, a bank statement or an application form, finds the information you need, checks it and enters it where it belongs, so your team stops typing it by hand.

Older tools worked from templates: draw a box where the invoice number sits and hope the supplier never changes their layout. AI reads a document the way a person does, by understanding what each number means, so a new supplier’s invoice or a phone photo of a delivery challan can be read without building a template first.

Reading is only half the job. What makes the result safe to use is what happens next: every value is checked against your records, and anything uncertain goes to a person.

In practice, every document passes through three steps. It is classified, so the system knows it is an invoice rather than a credit note or a delivery challan. Its fields are extracted, with tables read row by row. Then the values are validated, and the document either goes through or waits for review.

Start with the documents that arrive most often and are still typed in by hand. For most businesses that means supplier invoices. For lenders it means bank statements, salary slips and tax returns; for transport companies, delivery challans, lorry receipts and proofs of delivery; for clinics and insurers, claim forms and reports.

We count how many of each type arrive in a month, how long each takes to enter and what goes wrong today. A type that arrives hundreds of times a month and feeds an important decision, such as a payment or a loan, is usually the right first job.

Rare, very long or legally sensitive documents can still benefit through summaries and extracted key terms, but they are rarely the place to begin.

We need real documents, not perfect ones. A few hundred examples of each type, including the faded scans, the pages photographed at an angle, the invoices with handwritten corrections and the suppliers whose layouts annoy your team the most.

We also need the rules your team applies without thinking: which purchase order an invoice should match, how much difference in rate is acceptable, which suppliers are on hold and which fields your accounting software insists on. These become the checks.

Finally, we need one person who owns the process: someone in accounts, operations or credit who reviews the pilot results and decides when a document type is ready to go through on its own.

Documents rarely arrive as clean PDFs. AI document processing has to read scans, phone photos, statements that run to many pages and invoices whose line items continue onto a second sheet.

We combine a document-reading service, such as Amazon Textract, which finds text, tables and where they sit on the page, with an AI model that understands what each part means: which date is the invoice date and which is the due date, which code is the GSTIN, which table holds the line items. Printed Hindi, Gujarati and other Indian languages, and documents that mix them with English, are read the same way.

Every value keeps its position on the page, so the review screen can show exactly where it came from.

Checks turn reading into something you can trust. Each document is checked in three ways.

First, against itself: do the line items add up to the taxable value, and does the tax match the rate for each HSN code? Then against your records: is the supplier in your vendor list, is their GSTIN active, does the invoice match the purchase order and the goods actually received? Finally, against history: has this invoice, or one with the same number and amount, been seen before?

Indian documents bring checks of their own. An e-invoice carries a signed QR code with its IRN, the seller’s GSTIN, the invoice number, date and value, so the printed figures can be compared with what was reported to the GST system. E-way bill numbers can be checked against the consignment they travel with, and PAN and GSTIN formats are validated before anything is looked up.

A document that passes every check can go through by itself. One that fails any check waits for a person, with the reason written in plain words.

The review screen is where your team spends its time now, instead of typing. It shows the original document on one side and the extracted fields on the other, with every uncertain or failed field highlighted and linked to its place on the page.

A reviewer confirms or corrects the fields, adds a note if needed and approves. Corrections are recorded, so the same mistake on the next document from that supplier becomes less likely.

How much goes to review is your decision. Most businesses start with a person checking everything, then let clear documents from known suppliers through once the pilot shows they are read correctly.

Processed documents only help once the data is where your team works. We connect the output to Tally, your ERP, your accounting software, your loan system or even a shared spreadsheet, through their APIs or a bridge we build.

A purchase invoice becomes a voucher or a bill, linked to the original PDF. A bank statement becomes a set of figures in the underwriter’s case. A delivery challan closes the matching consignment. Nothing is posted until it has passed its checks or been approved by a person.

Not every document is a form. Contracts, leases, insurance policies, tender documents and technical manuals are long, and what people need from them is a handful of facts and a way to ask questions.

For these, AI document processing produces a summary with the key terms pulled out, such as the parties, dates, amounts, notice periods, renewal terms and penalties, each linked to the clause it came from, so a reviewer can check it against the original in seconds.

The same documents can be made searchable. Your team, or your customers, ask a question in plain words and get an answer drawn from the documents, with a link to the exact page. A summary never replaces reading a contract before signing it; it shows you where to look first.

Documents carry some of the most sensitive data a business holds: bank details, ID numbers, salaries and medical information. We handle them that way.

Files are encrypted in storage and in transit, processed on cloud services in the region you need, and visible only to the roles that need them. Numbers such as Aadhaar can be masked once checked, and documents are deleted on the schedule you set. AI providers’ business services are used, so your documents are not used to train their models.

Every view, correction and approval is logged with the person’s name, and personal data is handled in line with India’s data protection law, or the GDPR where the people in your documents are in Europe.

Accuracy is measured, not assumed. Before launch we run the system on a set of documents your team has already entered and compare every field. The result shows which fields can go through on their own and which still need a person.

After launch, a monthly report shows the documents processed, the share that went through without review, corrections by field and by supplier, and how long reviews took. New suppliers’ layouts, new document types and changes in tax rules are added as they come.

The share of documents that go through without anyone touching them is the number most teams watch. It should rise as corrections accumulate and known suppliers are trusted, while the documents that still need a person are the genuinely unusual ones.