Automated document processing with AI that reads, links, and fills in the rest

Invoices, contracts, reports, filings, internal summaries. Someone has to read all of it and pull out the numbers that matter. Then retype those numbers into a system, check that nothing contradicts anything else, and build yet another document out of the result. By hand, every time. Practically every company knows this loop, because documents reach practically every company.

McKinsey puts it at 19% of a knowledge worker's time, spent searching for and gathering information. One day a week, in other words, in which an accountant, a lawyer, or a compliance officer does work a machine could do. And that is only the visible half, because manual retyping also produces errors that someone then has to find and fix.

We decided to go at this systematically. rise.sk is building a platform that doesn't just read documents but understands them, links what it finds across them, and produces the outputs on the other end without a person in the middle.

What we're solving

The platform follows a document through the whole trip, from the moment it lands to the moment something new comes out of it.

It can land in any format, whether that's a PDF, a scan, an email attachment, a Word file, or a spreadsheet. The AI reads it, works out what kind of document it is, and pulls out what matters, which usually means amounts, dates, parties, company IDs, and contract numbers. Those values then get matched against records you already hold. An invoice finds its own supplier, its own framework agreement, and its own contact person, and from that same data the system fills a template, assembles a report, or produces a summary.

This isn't a chatbot that answers questions about a document. It's a pipeline that chews through hundreds of documents a day without anyone opening them one by one.

Three things the platform does

1. Reading and classifying

Understanding comes first. A document arrives and the model sorts it. Invoice, contract, delivery note, internal report? Each type is laid out differently and each hides different data.

Extraction follows. OCR (Optical Character Recognition) handles the scans, NLP (Natural Language Processing) handles digital text, and between them they lift out what you asked for. From an invoice, that's amounts, due dates, supplier and buyer IDs, and order numbers. From a contract, it's the parties, the subject matter, the deadlines, and the penalty clauses.

What matters is what the system leans on. It isn't memorising rigid templates. It runs language models that read context, so when a supplier redesigns their invoice, the right fields still come out. The model knows what a due date is. Where the due date happens to sit on the page is beside the point.

2. Linking entities

Extraction on its own is useful. What comes next is what makes it worth paying for. Every value the system pulls out gets matched against entities already sitting in your database.

Take an invoice from ABC Ltd. The system reads the company ID, finds the matching record, ties the invoice to the framework agreement that's currently in force, spots the contact person, and files it against the right project. Nobody clicked anything.

It runs the other way too. Open a client's card and everything connected to them is already there. Contracts, invoices, delivery notes, correspondence. Not because someone sorted it by hand, but because the system knows how the documents relate.

Two techniques do the work. Exact matching covers company IDs and contract numbers, where nearly right is the same as wrong, while fuzzy matching handles company names and addresses, which are spelled a little differently in every document that mentions them. The result is a relationship graph, and it gets denser with every document that passes through.

3. Generating reports and filling templates

Output is the last link. Once the data is extracted and the entities are linked, documents can be produced on demand.

You describe a template once. A contract, a quote, a handover protocol, a form, written out with variables in it, and from then on the system fills it with the right values. Periodic reports work much the same way, except the data is aggregated over a period first, which is how monthly overviews, quarterly reports, and compliance documentation get made. Summary reports round it out, whether that's billing per project, contract obligation status, or an accounts receivable aging report.

Templates stay yours. Your format, your fields, your logic. The system just applies them consistently, with no typos and with current data.

Who it's for

We're building this for organisations where documents aren't the exception, they're the job itself.

Accounting firms and finance departments get invoice processing, matching against purchase orders, and financial statements out of it. Nobody keys an invoice in by hand any more. The AI processes it and hands it over ready for approval.

For law firms it analyses contracts, lifts out the terms that matter, and watches deadlines. Standard contracts then get generated from client data the firm already holds.

In the public sector it's filings, checking whether documentation is actually complete, and drafting decisions and notices. An official doesn't have to dig through attachments to work out what's missing.

Large enterprises with complicated documentation use it for compliance reporting, internal audit, and supplier relationships. The linking is what pays off there, because the gaps between departments are exactly where information goes missing today.

Under the hood

Five components carry the architecture.

The Document Ingestion Layer takes documents from wherever they reach you. Email, API, upload, watched folders. It normalises the formats and gets each document ready to process.

The AI Processing Pipeline is the core. It combines an OCR engine for scans, a language model for classification and extraction, and an entity recognition module that spots entities and wires them together. We built it modular, which means any one piece can be swapped or improved without disturbing the rest.

The Entity Graph is a graph database holding the relationships. A company has contracts, contracts have invoices, invoices have line items, line items point back at purchase orders. Every new document adds a few more edges.

The Template Engine produces the documents and handles conditional blocks, calculations, and formatting. We deliberately kept templates in a simple format so someone who doesn't write code can still edit one.

The Validation & Review Layer watches quality. Anything with a low confidence score goes to a human instead of straight through. The more documents pass, the more accurate it gets.

Real use cases

Supplier invoices

A mid-size manufacturer takes in over 500 supplier invoices a month. Until now an accountant opened each one, retyped it into the accounting system, and matched it against a purchase order.

Now the invoices arrive by email at a dedicated address and process themselves. The system lifts the supplier, amount, VAT, due date, and order number, finds the matching purchase order in the ERP, and compares the two. If they agree, the invoice goes straight to the responsible manager for approval. If the amount is off or the supplier is unknown, it gets set aside for a human.

80% of those 500 invoices clear automatically. The accountant now spends half a day on invoices instead of three, and only on the ones that didn't fit.

Quarterly compliance reports

A financial institution has to file regulatory reports every quarter. The data for them is scattered across dozens of internal documents, in contracts, transaction records, client correspondence, and internal decisions.

With the relevant entities already linked, the system assembles that data itself at quarter end. It aggregates, fills the regulatory form, and generates the appendices. The compliance officer gets a finished report to check instead of a heap of raw data to compile.

Two weeks of work becomes two days.

Standard contracts

A law firm turns out dozens of standard contracts a month. Leases, service agreements, sale agreements. Each one needs client details, property information, and terms typed in by hand.

Instead the lawyer picks a contract type and a client, and the system fills the template from current database records. Party identification, addresses, subject matter, pricing from the quote. Out comes a draft for the lawyer to review and adjust.

Drafting a standard contract drops from 45 minutes to 5. Typos and inconsistencies go away, because nobody is retyping anything.

Why rise.sk

We're not the only ones doing document automation. The difference is what's behind us.

We put AI into production, not into pitch decks. We're not a research team writing papers, we're engineers who ship into live environments. We know what looks good on a slide and what survives contact with real data. Those are two different things.

We know Slovak documents, down to the invoice formats, the contract structures Slovak law prescribes, the company and VAT ID formats, and the habits of the public administration. Statute dictates the shape of a lot of this paperwork. On those documents the gap between a system that works and one that almost works is exactly this kind of knowledge, and a generic product translated in from abroad doesn't carry it.

We don't start big. One document type, one process, prove the value, then widen. Every step is measurable and every step has to pay for itself.

And security. Documents hold sensitive data, so the platform runs in a hardened environment, data is encrypted at rest and in transit, and access is managed and logged. Clients with the strictest requirements can have it deployed on-premise.

Pilot program

If your company burns hours a day processing documents by hand, get in touch. We're opening a pilot program to a small number of organisations. We take one specific document process, deploy the platform on it, and show measurable results.

It starts with analysis, mapping how you handle documents today and where the time actually goes. Then we configure the platform for your document types and your workflow, and test it on real data with the results held up against manual processing. At the end we measure the time saved, the error rate, and the reliability. The numbers decide what happens next.

Documents are the foundation of a business. It's about time they were handled intelligently. Get in touch and we'll set up a first call about what AI could change in the way your company handles documents.