Service
AI document processing: data from paper into your database, hands-free
An invoice, a bill, a form, a scan, a photo from a phone — a person reads it and types it into 1C or a spreadsheet by hand. That's hours of work and errors in the numbers. AI reads the document and puts the data where it's needed: 1C, CRM, Google Sheets. Stack: Node.js + OpenAI / Anthropic vision.
What we extract
Three types of documents where AI removes manual entry
- Invoices and bills
A supplier sends an invoice as a PDF or photo — AI reads the number, date, counterparty, line items, totals and VAT, and puts it into 1C or your accounting database. The accountant checks a ready-made card instead of typing it from scratch.
Good fit: wholesale, logistics, accounting — dozens of supplier documents a day.When it's NOT worth it: a handful of documents a week — manual entry is cheaper than implementation.
- Forms and applications
A passport, a contract, a completed form, a customer application — AI extracts the fields (full name, dates, details, address) and creates a record in the CRM or database. The manager doesn't retype anything, and works with the data right away.
Good fit: real estate, legal, financial services — a steady flow of forms and contracts.When it's NOT worth it: data already arrives in digital form — what you need then is integration, not document processing.
- Scans and photos
A receipt, a slip, a report, a handwritten note, a photo taken at an angle from a phone — AI reads what a regular OCR tool can't. The extracted data lands in a spreadsheet or system in the structure you need.
Good fit: retail, services, field teams — documents from the field in varying quality.When it's NOT worth it: the document format is different and unpredictable every time — organize the source first.
How it works
Extract, verify, log — a human sees only the ambiguous cases
What's included
- Recognition of any format — PDF, scan, photo from a phone, even at an angle and in shadow. AI reads the content, not a template, so it doesn't break when a document looks different.
- Extraction into your field structure — you specify the fields you need (counterparty, amount, date, line items), AI returns exactly those. Not "the text of the document", but ready data for your system.
- Confidence checks — where AI isn't sure of a value, it flags the field instead of guessing. Ambiguous cases go to a human, everything else runs automatically.
- Logging into 1C, CRM or Google Sheets — the data goes where you actually work with it. We write the integration for your system, not through a third-party service.
- Processing log — you see which document was parsed, which fields were extracted, what went to a human. Errors are easy to spot and the rule easy to fix.
- Stack: Node.js + OpenAI / Anthropic vision. Documents are processed under enterprise API terms — not used to train public models. NDA before the start.
How we work
First on your documents — then into production
Breakdown on your documents
You give us 20-30 real documents. We look at: the format, which fields need extracting, where to put them, how many documents per month. Deliverable: an assessment of what gets automated, which fields AI reliably captures, where a human stays in the loop, timeline and budget range.
Pilot on one document type
We take one document type — say, invoices — and build a working extraction pipeline. We run it against your real files and compare the result to manual entry. Deliverable: a pilot processing your document type + an accuracy report per field.
Integration and verification
We connect logging into 1C, CRM or a spreadsheet, and set up the "ambiguous — to a human" logic. We test on a larger volume, catching edge cases. Deliverable: a full flow from document to record + rules for which fields need confirmation.
Launch and accuracy control
We put it into production; for the first few weeks we review the log together: share of documents handled without a human, where AI misses, what to adjust. Deliverable: a working process in production + an accuracy metric + an agreement on support and new document types.
FAQ
What businesses ask about AI document processing
Discuss your project
Show us 20 documents — we'll tell you what gets automated
- 30 minutesOne-on-one online
- Flexible formatVideo or phone call
- Solution-focusedPractical answers
Learn more
When AI document processing and data extraction pay off, and when they don't
AI document processing (intelligent document processing, IDP) is the automatic extraction of structured data from unstructured documents: invoices, bills, forms, contracts, scans and photos. Apricode — a Ukrainian engineering team from Kharkiv, in the web since 2016, with clients worldwide — builds these solutions on Node.js connected to OpenAI and Anthropic vision models. Put simply: a document goes in as a file and comes out as a ready-made record in your system.
Where data extraction pays off quickly
The clearest effect shows up where an employee manually types the same type of document into a system every day. Wholesale and logistics companies receive dozens of invoices from suppliers. Accounting handles a flow of bills and reports. Real estate and law firms process a steady stream of forms and contracts. In all these cases, a person reads numbers off paper and types them into 1C or a CRM — slowly and with errors. AI document processing removes that routine, and the person moves to reviewing what's ambiguous. This is the clearest-payoff direction across our whole AI lineup — the value doesn't need explaining.Business process automation →
Why AI rather than regular OCR? Classic recognition is tied to a template: it knows the total sits in the bottom right corner, and breaks the moment an invoice from a different supplier arrives. AI reads the meaning of the document, not coordinates, so it handles documents of different appearance the same way, even handwritten notes. Where a separate parser used to be written for each format, now a single model works with a description of the fields you need.
When it's better not to start
Let's be honest: AI document processing doesn't always pay off. If you have a handful of documents a week and each one looks new, implementation will cost more than manually entering those same documents for a whole year. Data extraction loves volume and repetition: one or two document types, dozens or hundreds a month, predictable fields. If that's not your case, we'll say so at the breakdown call instead of selling a solution just to sell one.
Extraction is often the first step in broader automation. Once data from documents starts landing in a system automatically, the next logical question is what to do with it next: classify, route, assign tasks. That's what an AI agent handles, acting on rules over your data. And once enough data has accumulated, you can build analytics and forecasts on top of it. All of this is a sequence of steps, not one-off projects.AI agents for business →
Our own proof that this isn't theoretical is the AI module in Apros CRM, where a model processes incoming data every day. We bring the same approach to your documents: a pilot on one document type, per-field accuracy checks, then launch. If you want to see the scope of the whole direction, start with an overview of our AI solutions for business.AI for business: an overview →