Knovium article
From archive search to source-grounded answers
How enterprise RAG turns archives into verifiable answers with citations, pages, and document chains.
Read articleEnterprise RAG for archives and documents
Knovium turns archives into source-grounded answers
Teams are not looking for files. They need causes, decisions, requirements, and document chains. Knovium combines retrieval, industry ontology, and answer generation so every conclusion can be checked against a document, page, and passage.
Answers with source citations
Ontology for documents and assets
On-premise and private deployment
RU / EN / KK / TR / AR
How Knovium works
The system is deployed as an AI layer over existing repositories: ECM, SharePoint, network folders, PDF archives, scans, and spreadsheet attachments.
Connect
Connect sources without archive migration and preserve access rights.
Structure
Extract text, tables, metadata, pages, and links between business objects.
Retrieve
Find relevant documents, passages, table rows, and event chains.
Answer
Generate answers with verifiable citations, limits, and recommended next actions.
Where it matters most
Knovium is designed for complex archives where answer quality depends on relationships between documents, equipment, contracts, and events.
Interactive demo across industries
This is a chat over a synthetic Knovium archive. Pick an industry and a question — the answer is assembled from several documents with page numbers and a source chain.
Pilot economics
ROI is treated as a verifiable hypothesis, not a promised outcome. Before deployment, Knovium records the baseline: search time, share of answers supported by sources, and workflows that still require manual review.
20%reference estimate for knowledge-worker search time
30-50control questions for the pilot
3 layersretrieval, citation quality, business usefulness
Publications
Five full-length Knovium articles with translations, sources, tables, and charts for evaluating enterprise RAG before a pilot.
Knovium article
Why corporate archives need GraphRAG
Flat retrieval works until the question depends on one document. Real enterprise archives are networks: an asset is linked to a passport, defect report,...
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Knovium article
PDF, OCR, and tables: where enterprise RAG breaks
Enterprise RAG often fails before the model sees the context. Noisy scans, merged table cells, and engineering layouts can turn a precise document into...
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Knovium article
How to measure RAG quality before a pilot
A RAG pilot cannot be judged by fluent answers alone. It needs a matrix: correct document found, citation valid, answer complete, unsupported claims abs...
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Knovium article
A secure AI layer over corporate documents
Enterprise RAG touches contracts, personal data, technical schemes, and internal decisions. Security starts with access architecture, audit logs, and go...
Read articleA secure AI layer over corporate documents
Enterprise RAG touches contracts, personal data, technical schemes, and internal decisions. Security starts with access architecture, audit logs, and go...
Request a demo on your documents
Describe your archive, industry, and target scenario. The form is submitted through Web3Forms and keeps the extended request fields.