Extract
Every document yields entities: companies, people, amounts, dates, places, IBANs, reference numbers. Spellings are merged into canonical entities. Logos connect documents with senders.
Knowledge Graph · AI-DMS module
The knowledge graph connects documents, companies, people and reference numbers into a network of relations.
Order, delivery note, invoice, contract: one case instead of four files. Interactive, askable, readable via API.
Trusted by leading companies worldwide
How it works
The graph arises from processing. Nobody draws edges, nobody maintains tags.
Every document yields entities: companies, people, amounts, dates, places, IBANs, reference numbers. Spellings are merged into canonical entities. Logos connect documents with senders.
Relations arise from meta fields, embedding similarity and, from Ultra, LLM reasoning: invoice to order, delivery note to invoice, contract to party. Documents and entities become nodes, relations become edges.
Interactive graph with thumbnails, hover and click. Graph RAG answers questions across several hops: which invoices belong to this contract and who signed it? With sources.
Capabilities
What the graph connects and how you use it.
Invoice to order, delivery note to invoice, contract to party, without manual tagging.
Order, delivery note, invoice and payment as a chain over reference numbers.
Spellings of companies and people into one entity, stable on reprocessing.
Questions across several hops: vector search, full text and graph expansion, with sources.
Sender via logo and letterhead, even when the name is missing or differs.
Invoice, order and contract numbers as their own nodes with all documents.
Cases chronologically, filterable by partner and period.
Thumbnails as nodes, hover for details, click for preview, colored edges per type.
Comparison
The difference is who creates the relations and whether they can be questioned.
| Comparison | Manual tags | Links in the DMS | PaperOffice knowledge graph |
|---|---|---|---|
| Creation | By hand | By hand per document | Automatically from processing |
| Entities | Free text | Master data | Canonically resolved |
| Cases | Not covered | Individual links | Chains over reference numbers |
| Questions | Filters only | Navigation only | Graph RAG across several hops |
| Maintenance | Permanent | Permanent | None |
curl -X GET \ "https://api.paperoffice.ai/latest/knowledge_graph/universe?workspace_id=1553&max_nodes=120&compact=true" \ -H "Authorization: Bearer po_ut_YOUR_API_KEY" curl -X GET \ "https://api.paperoffice.ai/latest/knowledge_graph/partner/Nordlicht%20Energie%20GmbH" \ -H "Authorization: Bearer po_ut_YOUR_API_KEY" curl -X GET \ "https://api.paperoffice.ai/latest/knowledge_graph/business_case?reference=RE-2026-0917" \ -H "Authorization: Bearer po_ut_YOUR_API_KEY" For developers
Universe per workspace, partners with all documents and cases per reference number as endpoints.
Nodes and edges per workspace, size and similarity threshold selectable, compact or with details.
All documents and relations of a business partner or a reference number.
Graph, timeline and references as tools in Claude, ChatGPT and Cursor.
Security and compliance
Every edge traces back to the documents it arose from. The graph is stored in our own EU data centers and shows only what the user may see.
Frequently asked questions
From three sources: meta fields such as reference numbers and parties, embedding similarity between documents and, from Ultra, LLM reasoning that assigns invoice, order, delivery note and contract. Every edge names its source.
A search that combines vector search, full text and graph expansion. Questions are answered across several hops, for example from the contract via the party to the open invoices, with sources per answer.
No. The graph arises from processing from Premium. Ultra adds LLM relations and vision recognition.
All documents for one reference number: order, delivery note, invoice, payment. The graph shows them as a chain with a timeline.
Entity resolution merges them via publisher, email, company name and VAT ID into one canonical entity. Logos from vision analysis confirm the sender.
Yes. Universe per workspace, partner, case, timeline, references, types and statistics are available via the REST API and in the MCP server.
Measurable customer success
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