AI application

AI for Documents & Knowledge: source-grounded answers from your files

Documents & Knowledge is the AI-driven capture, analysis and semantic search across business documents, from invoice extraction to source-grounded knowledge search via Retrieval Augmented Generation (RAG). Beyonetix runs this pipeline on its own servers in Germany, low-hallucination and with source citations.

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Overview

Documents & knowledge at a glance

The field combines four core components into one end-to-end pipeline: document recognition (OCR plus layout parsing), structured data extraction from invoices, contracts and receipts, semantic search and classification over vector databases, and GoBD-oriented, audit-proof archiving with audit trails. Unlike classic OCR, modern systems understand document structure, tables, fields, signatures, and work template-free even on changing invoice layouts where rigid templates fail.

The economic lever is concrete: manual invoice capture typically costs 15 to 40 EUR per document, while AI-based extraction often cuts that to under 0.50 EUR, and processing time drops from minutes to seconds. According to a Quocirca survey, 63 percent of companies plan to increase their investment in intelligent document processing (IDP). In industry tests, modern IDP engines reach 98 to 99 percent field-level accuracy on structured invoices and over 95 percent on scanned mixed-quality documents, production-ready for many use cases.

The decisive building block is RAG: vector databases index your documents by meaning similarity rather than keywords. When a question is asked, the system finds the relevant passages and feeds them as context to the language model, which then generates an answer drawn solely from your actual documents, with source citations. This substantially reduces hallucinations. Beyonetix runs such a citation-grounded RAG pipeline in production in a large-scale AI archive with millions of documents and applies the same principle to documents and knowledge in the Mittelstand.

Invoice processing
Contract & risk review
Semantic knowledge search
GoBD archiving

RAG · 0-Halluzination

Use cases

Where it creates value

Invoice processing A trades business with 50 suppliers automatically extracts invoice number, date, line items and taxes, maps them in a DATEV-compliant way and triggers approval workflows. Processing time: around 10 seconds instead of several minutes per document.

Contract & risk review A law firm or mid-sized company with 200 active contracts scans its entire contract landscape in minutes for termination clauses, missing definitions and regulatory gaps. The AI flags risks for review; the final judgement stays with the lawyer.

Semantic knowledge search A manufacturer with 500 employees asks its RAG system "Which approvals do I need for a new product version?" and gets a source-cited answer from manuals and policies in seconds, instead of lengthy keyword searches.

GoBD archiving Companies subject to bookkeeping duties have incoming documents classified automatically (invoice, quote, correspondence), tagged with metadata by rule and stored immutably with an audit trail, ending file chaos and supporting tax-authority requirements.

Document classification A service provider with high mail volume automatically separates incoming PDFs and scans by document type and routes them into the correct process, without anyone manually reviewing and sorting every file.

Employee knowledge assistant A company with a large knowledge base of policies, meeting minutes and project reports gives employees an assistant that answers questions in context and with citations, noticeably speeding up information retrieval.

Technology

Technologies & methods

Extraction

OCR
Layout parsing
NER
Classification

RAG

BM25
Vector embeddings
Reranking
NLI-Grounding
PageIndex

Storage & compliance

Qdrant
Neo4j
PostgreSQL
GoBD
WORM

What we deliver

From idea to a production application

Hosting in Germany The entire pipeline runs on Beyonetix's own servers in Germany. Your contracts and receipts never leave your control and do not flow into third-party model training, mindful of GDPR and the EU AI Act, with an on-premises option.

Open, self-hosted models Beyonetix runs open models such as Mistral, Qwen and Teuken self-hosted with vLLM behind a LiteLLM gateway, by default without dependence on public US APIs.

Source-grounded RAG answers Citation-grounded RAG with PageIndex and a knowledge graph delivers answers from your documents, each with a source citation. The approach is in production in a large-scale AI archive with millions of documents.

Template-free extraction Data extraction from invoices, contracts and receipts works on variable layouts because the models understand document structure, not just recognise characters like classic OCR.

Audit-proof trails Every extraction and classification is logged. This supports GoBD process documentation and makes AI decisions traceable for tax inspections.

Access control & honesty Permissions apply down to the vector index so RAG never reveals data the asker is not allowed to see. Beyonetix works transparently, clear limits instead of overblown promises, and no pretended certificates.

Sovereign knowledge management with source-grounded AI

Documents & Knowledge connects large language models with your own files and delivers fact-based, low-hallucination answers with source citations. The technical architecture has three layers: a capture layer of OCR and AI layout understanding that recognises tables, fields and signatures; an extraction layer in which language models interpret structured field values and semantics, such as the risk level of a contract; and a knowledge-integration layer based on Retrieval Augmented Generation (RAG).

RAG is the core: vector databases index your documents by meaning similarity rather than keywords. When a question is asked, the system finds the relevant passages and hands them to the model as context, which then answers from actual documents. This is exactly where the sovereignty advantage lies: when confidential contracts and receipts feed the model, that should not happen via third-party public APIs in unknown data centres.

That is why Beyonetix runs this pipeline on its own servers in Germany. Open models such as Mistral, Qwen and Teuken run self-hosted with vLLM behind a LiteLLM gateway, by default without dependence on public US APIs. Your data never leaves your control and does not flow into third-party training. This is sovereign AI that consciously accounts for GDPR and the EU AI Act, with an on-premises option for especially sensitive holdings.

Source-grounded: citation-grounded RAG with PageIndex and a knowledge graph, proven in production in a large-scale AI archive with millions of documents.
Template-free: extraction on changing invoice layouts where rigid templates fail.
Audit-proof: audit trails for every extraction, aligned with GoBD process documentation.
Access-controlled: permissions apply down to the vector index so no data reaches unauthorised users.

We communicate honestly where the technology has limits: handwritten receipts, deliberately vague contract language and highly specialised documents remain cases for human review. The AI flags and handles much of the analysis, the final decision rests with authorised people. Beyonetix is based in Chemnitz and supports the DACH Mittelstand from concept to production operation.

Frequently asked

Questions about documents & knowledge

Does Beyonetix process our documents on US servers or use them for model training?

No. Beyonetix runs the documents-and-knowledge AI on its own servers in Germany using self-hosted open models (e.g. Mistral, Qwen, Teuken). By default no public US APIs are used, and your data does not flow into third-party training. An on-premises option is available.

How reliably does the AI recognise our documents?

On structured standard invoices and DATEV layouts, modern IDP engines reach 98 to 99 percent field-level accuracy in industry tests, and over 95 percent on scanned mixed-quality documents. With handwriting or very old receipts, accuracy drops to around 75 to 85 percent, usually still better than manual capture, but it requires review loops for exceptions.

When does AI invoice processing pay off?

The economic lever depends on volume. Manual capture typically costs 15 to 40 EUR per invoice, AI extraction often under 0.50 EUR. At 500 or more invoices per month the effect is significant; below roughly 300 documents a month the business case should be examined critically.

How does RAG prevent the AI from inventing answers?

With Retrieval Augmented Generation, the system first finds the relevant passages in your documents and passes only those as context to the language model. The answer is generated from actual documents and includes source citations. This substantially reduces hallucinations, though it does not eliminate them entirely. Beyonetix uses this citation-grounded architecture in production in a large-scale AI archive with millions of documents.

Is AI document processing GoBD- and audit-compliant?

GoBD requires process documentation of what the AI did, including an audit trail. Systems that log every extraction and classification and store documents immutably support this. The tax office can always review data entry; audit-proof documentation helps make the process demonstrable. The GoBD-compliant implementation is something you handle together with your tax advisor.

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