The problem
Your company runs on documents: statements, legal files, contracts, policies. Generic "chat with your PDFs" tools break down on this work. Extraction errors go unnoticed, nothing can be audited, and data leaves the approved perimeter.
Service
Document AI and RAG for the documents your company runs
I build document AI and RAG systems: OCR and LLM extraction, validation checks and audit trails, deployed in your EU cloud or on your own OpenShift. Clients include banks and other regulated companies. One pipeline processes more than 2,500 financial documents a day and cut manual work by 84%. Another cut handling time on inheritance checks by 80%.
Key numbers
2,500+financial documents processed a day
−84%manual work on financial statement extraction
−80%handling time on inheritance document checks
81%accuracy across 400+ help desk categories
What I build
- Extraction pipelines with OCR + LLM and explicit validation checks for each document
- Straight-through processing: documents that pass every check are cleared, and exceptions go to an operator with the reason
- RAG over your internal knowledge (procedures, taxonomies, policies) for agents and assistants
- An audit trail on every production document, and no sensitive data in logs
Architecture and stack
Azure Document Intelligence, Azure OpenAI, LangChain; FastAPI + Kafka with horizontally scaled workers; Docker, Kubernetes, OpenShift; Qdrant, Pinecone, ChromaDB; PostgreSQL, MongoDB.
Results
- Financial statement extraction: 2,500+ docs a day, ~30 checks each, −84% manual work
- Inheritance document checks: −80% handling time at a major Italian bank
- Help desk RAG agent: 81% accuracy across 400+ categories
How to judge document AI
Extraction accuracy is only part of the picture. The number to watch is how many documents clear automatically with no errors slipping through. Validation checks, exception routing and audit trails drive that number, and they are also what gets the system past compliance.
Frequently asked
What is document AI?
Software that reads documents (scans, PDFs, forms), pulls out structured data and checks it. Where the rules are strict, it combines OCR, an LLM to interpret the content and explicit validation rules. One pipeline in production processes over 2,500 financial documents a day, with about 30 checks per document.
What is the difference between RAG and document extraction?
Extraction turns each document into structured fields for a process, like the figures in a financial statement. RAG pulls the relevant passages when a question comes in, so an LLM can answer or decide using your own knowledge, like the category definitions used to route tickets. Many systems need both.
Can document AI comply with GDPR and our internal policies?
Yes, if it is designed for it. In my banking projects the models ran on Azure OpenAI inside the client's EU cloud, sensitive data never went into the logs, and every production document had an audit trail. Pipelines can also run on the client's own OpenShift.
How do you prevent LLM extraction errors?
Never trust a single pass. Each document goes through validation checks, about 30 in the financial statement pipeline, and only documents that pass all of them are cleared. When a check fails, the document goes to a person with the specific reason.
How much manual work can document AI remove?
In projects running in production: 84% less manual work on financial statement extraction, and 80% less handling time on inheritance document checks at a major Italian bank.
Related
Next step
Buried in documents? We can find out which ones AI can read for you.
Tell me the workflow. We scope it on a free call, then a working version is live within 10 days.
Book a scoping call