From OCR to AI Agents: How We Automate Document Workflows for Our Clients

The problem: mountains of documents, little time

Many of our clients (professional firms, logistics companies, manufacturing SMEs) receive dozens or hundreds of unstructured documents every day: invoices, delivery notes, contracts, hand-filled forms. Manual transcription is slow, expensive, and error-prone.

In recent projects we've built pipelines that eliminate most of this manual work, while keeping the level of control and reliability needed in contexts where a mistake has a real cost (billing, contracts, compliance).

A three-layer architecture

1. Ingestion and OCR

The first step converts the document (PDF, photo, scan) into raw text. We use different OCR engines depending on the case: for clean documents (native PDFs) direct text-layer extraction is enough; for scans and photos we rely on OCR with perspective correction and denoising, essential for forms photographed with a phone under imperfect lighting.

2. Structured extraction with an LLM

Raw text alone isn't enough, we need to understand what it represents. This is where an LLM with a constrained output schema comes in, mapping the text to a defined set of fields — invoice number, VAT ID, line items, totals, due dates. Schema validation lets us catch inconsistent extractions immediately (e.g. a total that doesn't match the sum of line items) before the data ever reaches the client's systems.

3. An AI agent for orchestration

The final layer is an agent that decides the next action: if extracted data passes confidence thresholds, it proceeds automatically (e.g. logs the invoice in accounting software, updates a CRM, sends a notification); if there's ambiguity, it creates a human review task with the original document and the uncertain fields highlighted. This "human in the loop" pattern is what makes the solution reliable in production, not just in a demo.

What we learned

• Not everything should be 100% automated. The real value is reducing human work to exception-handling only, not eliminating it entirely.
• Validation matters more than the model. A strict output schema and post-extraction consistency checks prevent more errors than a "better" prompt ever could.
• Observability from day one. Logging every extraction with its confidence score lets us measure accuracy over time and know where to intervene.

When it makes sense

This kind of automation pays off when document volume is high, the format is recurring but not perfectly standardized, and the cost of an error justifies a selective layer of human review. If your team is still handling these flows by hand, there's likely room to reclaim hours every week.