Knovium

Enterprise 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.

In oil and gas, a single asset moves through dozens of documents: equipment passport, defect reports, work orders, procurement contracts. Knovium links them into a chain so an engineer gets a "why was it replaced" or "what confirms the downtime" answer in seconds instead of digging through the archive by hand. This matters most during incident reviews and regulatory inspections, where every conclusion needs to rest on a specific document and page rather than an engineer's memory. The larger the field and the longer the equipment history, the more time a team typically loses assembling evidence by hand — and the bigger the payoff from a ready-made answer chain.

See the demo for this industry →
Oil and gas

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.

K Knovium Assistant online

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 time saved by skilled staff on search
30-50control questions for pilot acceptance
3 layersretrieval, citation quality, business usefulness
1

Discovery + MVP

4-6 weeks

An archive audit and a working MVP on a slice of real documents — to test the hypothesis before a bigger investment.

$6,000–10,000

2

Pilot

8-12 weeks

The MVP is rolled out to a real user group and a broader slice of the archive, under near-production conditions.

Priced after Discovery + MVP

3

Full rollout

per agreed plan

Rollout across the organization based on the accuracy and coverage metrics validated during the pilot.

Priced after the pilot

We don’t quote the next stage’s price up front — it is set based on what Discovery + MVP finds in your archive, and later on the pilot’s results.

Discovery + MVP: price
6,000–10,000 $

4-6 weeks · up to 5,000 documents · up to 50 users

  • Baseline: how much time search currently takes
  • 30-50 control questions with citation checks against sources
  • An accuracy report and a go / no-go recommendation for the pilot

EUR/GBP/RUB/KZT rates are approximate and shown for reference only; the exact amount and contract currency are fixed in the commercial proposal.

Publications

Enterprise archive documents connected to an AI answer with sources
Knovium article

From archive search to source-grounded answers

How enterprise RAG turns archives into verifiable answers with citations, pages, and document chains.

Read article
A graph of links between documents, equipment, and decisions layered over an enterprise archive
Knovium article

Why corporate archives need GraphRAG

Three documents, three facts, but not one story. GraphRAG links them into a chain so you see not just the text but the asset it is about.

Read article
A scanned document and a table losing structure during text extraction for RAG
Knovium article

PDF, OCR, and tables: where enterprise RAG breaks

A pilot on ten clean PDFs says nothing about the rest of the archive. Usually most of it is scans and complex tables.

Read article
A matrix of RAG quality metrics: relevance, faithfulness, citation, latency
Knovium article

How to measure RAG quality before a pilot

"The pilot went well" is a sentence with no numbers in it. What is needed is a metric matrix, not a general impression from a few good answers.

Read article
Security control levels for an AI layer: RBAC, audit trail, governance
Knovium article

A secure AI layer over corporate documents

An accurate quote from a contract you have no access to is still a data leak — it just travels through a chat window instead of a file share.

Read article

A secure AI layer over corporate documents

An employee asks the AI assistant a question about a contract they have no role-based access to — and gets an accurate, well-cited answer with the full text of the terms. Formally, the system worked perfectly: it found the document and answered with a source. In substance, it's a data leak — it just traveled through a chat window instead of a file share.

Access-control maturity model for AI answers
No controls
35%
RBAC
58%
Audit trail
76%
Governance
88%

Illustrative 0-100 scale showing the share of typical access and data-leakage risks addressed at each control level; used as a reference point when designing the security architecture with a client.

Frequently Asked Questions

Everything you need to know about Knovium, GraphRAG architecture, security, and enterprise pilots.

What is Knovium and what problems does it solve?

Knovium is an enterprise AI layer and knowledge assistant built for complex technical archives, scanned records, spreadsheets, and engineering documents. Unlike basic keyword search or generic chatbots, Knovium delivers verifiable answers directly citing exact documents, pages, and excerpts.

How does Knovium differ from standard RAG implementations?

Standard RAG retrieves isolated text chunks and struggles with scanned PDFs, tabular structures, and document versions. Knovium uses hybrid GraphRAG with domain ontology, interconnecting equipment, defect acts, maintenance orders, and contracts into a unified knowledge graph with proven provenance.

What document formats and repositories does Knovium support?

Knovium integrates into existing enterprise storage (EDMS, SharePoint, network drives, relational databases) with zero migration required. It supports native and scanned PDFs (via high-accuracy OCR), technical drawings, Excel/CSV spreadsheets, Word files, and image attachments.

Can Knovium be deployed 100% On-Premise in air-gapped networks?

Yes. Knovium fully supports 100% On-Premise and Private Cloud deployments in air-gapped environments without external internet connectivity. All vector indices, document graphs, and local LLMs run strictly inside your security perimeter under enterprise access control (RBAC/ABAC).

How much does the Discovery + MVP phase cost, and how is the pilot priced?

The initial Discovery + working MVP phase runs for 4–6 weeks at a fixed price of $6,000–$10,000 (up to 5,000 documents and 50 users) to establish baseline search speed, validate 30–50 benchmark questions, and measure verified ROI. The full-scale pilot (8–12 weeks) follows as Phase 2, with custom pricing calculated from Discovery results based on actual archive complexity.

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.

Message us on WhatsApp

Form data is used only to organize a demo and discuss a pilot.