Ask
In natural language, by text or voice, across one or several workspaces. The question is understood, not just matched by words.
Document Chat · AI-DMS module
Document chat answers questions across the whole workspace and shows the source document for every answer.
Semantic search, full text, entities and knowledge graph in one answer. By text or voice, in app, mobile app and MCP.
Trusted by leading companies worldwide
How it works
The chat sits on the index the AI-DMS builds anyway. No upload per question, no context limit of a single document.
In natural language, by text or voice, across one or several workspaces. The question is understood, not just matched by words.
Five sources in one search: embeddings for meaning, full text, OCR text, entities such as companies and amounts, relations from the knowledge graph. Hits are merged with weighting.
The answer names every document used with page and relevance and opens it on request. The chat can filter the document view: show all invoices from October.
Capabilities
Not one PDF in a context window, but the workspace as a knowledge space.
Embeddings, full text, OCR text, entities and knowledge graph, weighted into one hit list.
Answers that connect invoice, contract and delivery note, with relations from the knowledge graph.
One question across all released workspaces, results separated by workspace.
Every answer names document, page and relevance and links the original.
Embeddings for summary, page, paragraph and metadata so details are found too.
Speak questions, hear answers, search without a keyboard.
The chat filters the document list: show all invoices above 1,000 euros from October.
The same search in Claude, ChatGPT and Cursor, with the same permissions.
Comparison
Any model can question one document. The difference starts with the second.
| Comparison | PDF in an AI assistant | Full-text search in the DMS | PaperOffice document chat |
|---|---|---|---|
| Scope | One document per chat | All, but words only | All documents, by meaning |
| Relations | None | None | Knowledge graph and entities |
| Sources | Without page and score | Hit list | Document, page, relevance |
| Memory | Ends with the chat | None | Across sessions |
| Actions | None | Open | Filter, open, send |
| Data location | Upload to the vendor | In the DMS | EU data centers |
curl -X POST \ "https://api.paperoffice.ai/latest/document_intelligence/embeddings/search" \ -H "Authorization: Bearer po_ut_YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"query":"Rechnungen Nordlicht über 1.000 Euro im Juli","embedding_type":"document_summary","limit":10}' curl -X POST \ "https://api.paperoffice.ai/latest/documents/documents-list" \ -H "Authorization: Bearer po_ut_YOUR_API_KEY" \ --data-urlencode "workspace_id=1553" \ --data-urlencode "global_search=Wartungsvertrag Kündigungsfrist" \ --data-urlencode "search_mode=hybrid" { "mcpServers": { "paperoffice": { "url": "https://mcp.paperoffice.ai/dms", "headers": { "Authorization": "Bearer po_ut_YOUR_USER_TOKEN" } } }} For developers
Semantic search over embeddings, hybrid document search in the workspace and the MCP server for your own assistants.
Question in natural language, selectable embedding layer, hits with score.
Full text, OCR, entities and embeddings in one call, with filters and pages.
Search, documents and actions as tools in Claude, ChatGPT and Cursor.
Security and compliance
Questions, answers and index are processed in our own EU data centers. The chat sees only workspaces the user is authorized for.
Frequently asked questions
No. The chat works on the index of the AI-DMS. Everything in the workspace that was processed from Premium is immediately askable.
With five sources in one search: embeddings for meaning, full text, OCR text, entities such as companies, amounts and dates, plus relations from the knowledge graph. Hits are weighted and merged.
Every answer names the documents used with page and relevance and links the original. Without a solid source the chat says so instead of guessing.
Yes. You choose one or several released workspaces, the answer separates sources by workspace.
Yes. It filters the document view, opens documents, sends them protected or hands over to chat and ticket agent. Permissions apply as in the app.
Embeddings are created from Premium. Basic documents are findable via full text and OCR text, Ultra adds knowledge graph and entities.
Yes. Semantic search over embeddings, hybrid document search and the MCP server are available.
Measurable customer success
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