Qdrant — the vector database powering AI in 4softerp ERP
Semantic search in fiscal documents, automatic duplicate invoice detection, intelligent accounting account suggestions and the AI assistant in 4softerp all run on Qdrant — the open-source vector search engine with the best performance-to-resource ratio in its category.
How Qdrant brings AI to financial ERP
Semantic search in fiscal documents
A user can search "Q1 fuel supplier payments" and Qdrant returns relevant documents from thousands of transactions, even without an exact lexical match — through embedding similarity.
Automatic duplicate invoice detection
Each received invoice is vectorized and compared against existing vectors. Similarity > 0.92 triggers a potential duplicate alert before registration.
Chart of accounts suggestions
When entering a new item, the embedding model maps the description to the most likely account in the chart of accounts — reducing classification errors by ~60% compared to manual selection.
Semantic partner matching (fuzzy lookup)
Searching for a partner by partial name or abbreviation finds the correct entity even with spelling variations or abbreviated names in the database.
Contextual AI assistant for transactions
The built-in chatbot in 4softerp has access to document vectors via Qdrant — it answers questions like "what is the balance of account 401 for supplier X" without raw SQL access.
Separate collections per tenant (full isolation)
Each 4softerp client has a dedicated Qdrant collection, with company_id payload filter. One tenant's vectors are not accessible to others — complete isolation at vector store level.
FAQ about Qdrant in 4softerp
What embedding model does 4softerp use with Qdrant?
Compact multilingual models (sentence-transformers/paraphrase-multilingual) run on-premise, without sending financial data to external APIs.
Does Qdrant access raw financial data?
No. Qdrant stores only vectors and non-sensitive metadata (document ID, type, date). Financial data remains exclusively in PostgreSQL.
How many documents can be indexed in Qdrant?
The current architecture supports up to 50 million vectors per collection with HNSW indexing — enough for 10+ years of documents for a large company.
Does AI search work offline / on-premise?
Yes. 4softerp Enterprise runs Qdrant and embedding models fully on-premise. No dependency on external cloud for AI features.
Real AI in ERP, not buzzword
Try the AI assistant and semantic search in 4softerp free for 6 months. No credit card required.