Graph Intelligence – agentic where it matters

Understand. Connect. Explain.

Corporate knowledge is contained in thousands of documents – yet it is often unclear what AI-generated answers are based on. Two (agentic) engines make this knowledge usable: the Knowledge Engine interprets terms in context, organises documents as a knowledge graph and identifies contradictions. The Evidence Engine answers domain-specific questions exclusively from verified content. Every statement cites its source and the relevant document context.

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Our products

Two engines that build on each other: first comes a properly understood, consistent body of knowledge, then evidenced answers drawn from it. Both developed, implemented and – if you wish – operated by us.

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Knowledge Engine

Understands what the terms in your estate really mean.

Rather than labelling each word once and assuming that label holds everywhere, it resolves the meaning of a term from its context. It does so by organising the document estate as a knowledge graph: terms, documents and cross-references form a network in which the connections themselves carry information. An (agentic) verification process then traverses the entire estate and flags whatever does not fit: contradictory statements, outdated versions, empty placeholders, missing references. The result is not a one-off finding, but a permanently understood, consistent body of knowledge whose state is documented at all times.

Meaning arises from context, not from the individual word
The estate is organised as a knowledge graph and systematically verified
Contradictions, gaps and outdated content are flagged with evidence

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Evidence Engine

Makes every statement traceable – and gives its reasoning.

It answers domain-specific questions solely from your organisation's knowledge: the language model only phrases what has first been found and verified in the estate. Knowledge and language capability remain strictly separate. The content is held in a controlled layer outside the model: addressable, purpose-restricted and demonstrably deletable. Every answer carries its sources, every answer is reproducible, and its completeness is measured, not assumed. Where no reliable source exists, the engine says so openly: a controlled admission of not knowing, rather than answers that sound plausible.

Every statement sourced, reproducible and verifiable in seconds
Knowledge remains outside the model: updatable, purpose-restricted, deletable
No source, no answer. A feature, not a bug

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The challenge

Corporate knowledge lives in documents – and documents contradict one another. Any estate contains outdated versions, forgotten placeholders, missing references and terms that mean different things depending on the context: "security", for example, can mean collateral, IT security or occupational safety.

Keyword-based search finds passages of text, not meaning. And chatbots answering from such an estate produce plausible-sounding answers without evidence. Nothing more.

Our answer

First understand, then answer. The Knowledge Engine identifies the sense in which a term is being used at any given point, and uncovers contradictions and gaps across the estate. On that foundation, the Evidence Engine answers domain-specific questions exclusively from your corporate knowledge – every statement with its source and context.

What follows from this stays with you: the engines deliver evidenced answers and well-ordered knowledge estates. Assessment and decision-making remain firmly in human hands.

What we offer

From a scattered estate to an evidenced knowledge system – all from a single source.

✓ Building and verifying your corporate graph (Knowledge Engine).
✓ An evidenced answering system built on your knowledge estate (Evidence Engine).
✓ Continuous quality measurement: completeness is demonstrated, not guessed.

✓ Findings on contradictions, placeholders and outdated versions – evidenced, not estimated.
✓ Privacy-compliant operation: knowledge that is addressable, purpose-restricted and deletable.
✓ Operation and further development by the team that wrote the methodology.

In brief

Four key Graph Intelligence terms explained briefly: meaning in context, knowledge graphs, RAG and source references.

Meaning in context

The same word can mean different things in different places. Using the surrounding context, the engine determines which meaning is intended – rather than treating every occurrence the same.

What is a knowledge graph?

A knowledge graph links terms, documents and references in a network where the relationships themselves carry information, revealing connections, contradictions and gaps.

What is RAG (retrieval-augmented generation)?

RAG provides a language model with relevant content retrieved from selected documents. The model answers only from verified passages and cites the corresponding sources.

What is a source reference?

A source reference is the specific evidence supporting a statement, including the document, paragraph and version. It enables teams to verify answers quickly and precisely.

FAQ's

How does the Evidence Engine reduce AI hallucinations?

The Evidence Engine reduces AI hallucinations by answering only on the basis of reliable evidence from your organisation’s knowledge. Every statement is linked to its source and document context. If no reliable evidence is available, the engine reports this rather than generating an unsupported answer.

How does the Evidence Engine make AI answers traceable?

The Evidence Engine makes AI-generated answers traceable through complete source references and documented verification steps. Every answer identifies the document, paragraph and version. This enables subject-matter experts to verify the evidence behind each statement and understand how it was produced.

Do documents need to be prepared before using the Knowledge Engine?

No, the documents do not need to be prepared in advance. The Knowledge Engine analyses the existing document estate and identifies contradictions, outdated content, placeholders and missing references. The findings show where clean-up is required.

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