Enterprise
Knowledge Fabric
Basic RAG is not enough for the enterprise. We build a multi-layer knowledge infrastructure - knowledge graphs, layout-aware parsing, and freshness governance - that eliminates the hallucinations that come from ignorance, staleness, and structural blindness.
The $31M Dark Knowledge Problem
A hospital network's clinical decision-support agent cited a drug interaction protocol that had been retired 8 months earlier. The PDF existed in storage but was never re-ingested after the policy update. The vector embeddings were stale. The agent had no freshness signal. It confidently cited outdated guidance as current fact - and the liability exposure from those recommendations cost $31M in remediation and legal settlements. Vector search alone cannot solve the enterprise knowledge freshness crisis.
The Dioval Knowledge Stack
Beyond RAG. Beyond Vector Search.
Three interconnected layers that give your agents accurate, structured, and temporally-valid knowledge - regardless of document volume or regulatory complexity.
GraphRAG & Multi-Hop Reasoning
Vector search finds similar text. Knowledge graphs find relationships. We implement Neo4j property graphs so your agents can traverse complex, multi-document regulatory hierarchies, cross-referenced legal contracts, and interconnected policy frameworks.
When a regulation in Document A references a definition in Document B which governs a procedure in Document C - our GraphRAG traversal follows those links automatically, delivering the complete answer rather than a fragment.
Layout-Aware Document Intelligence
Standard PDF parsers scramble multi-column regulatory layouts, destroy table structure, and lose reading order when documents mix text and figures. The result: your agent receives a garbled text dump and hallucinates to fill the gaps.
We build Document Intelligence pipelines using layout-aware models that preserve document hierarchy, reading order, table relationships, and footnote associations - delivering the document structure an LLM can actually reason over.
Section 4 Risk Classifi 4.1 High Risk Systems cation Systems listed in Annex III shall be considered high-risk AI sys 89% confidence tems. Prohibited AI Practices Article 5...
{
"section": "4. Risk Classification",
"subsection": "4.1 High Risk Systems",
"body": "Systems listed in Annex III...",
"cross_refs": ["Annex III", "Article 5"],
"confidence": 0.99,
"layout": "single_column"
}Knowledge Freshness Governance
Enterprise truth has an expiration date. Policies are revised. Regulations are amended. Clinical protocols are retired. We build incremental ingestion pipelines with SHA-256 change detection and per-document freshness scoring.
Every knowledge chunk carries a freshness score, a TTL, and a source version hash. Agents query not just for relevance - but for current relevance. Stale chunks are automatically flagged, quarantined, or re-ingested on schedule.
How Fresh Is Your Knowledge? When Did You Last Check?
Our Production Readiness Audit includes a full Knowledge Freshness audit - we test your RAG pipeline for staleness, structural accuracy, and multi-hop retrieval failures. The results are rarely comfortable.
Audit Your Knowledge Infrastructure →Frequently Asked Questions
What is the Dark Knowledge Problem in enterprise AI?
The Dark Knowledge Problem refers to the vast amount of critical institutional knowledge trapped in unstructured documents, tribal expertise, and siloed systems that AI agents cannot access. In most enterprises, agents only see 20–30% of relevant context, leading to hallucinations, missed constraints, and costly errors.
What is GraphRAG and how is it different from vector search?
Vector search retrieves documents based on semantic similarity, but it cannot reason across relationships. GraphRAG combines knowledge graphs with retrieval-augmented generation, enabling multi-hop reasoning: connecting a regulation to a product to a risk to a control, across multiple documents and domains.
How do you prevent AI hallucinations in enterprise?
Hallucinations stem from incomplete or stale knowledge. We implement three countermeasures: GraphRAG for multi-hop factual grounding, layout-aware document intelligence for precise extraction, and knowledge freshness governance with automated staleness detection and re-ingestion pipelines.
What is a Knowledge Fabric?
A Knowledge Fabric is Dioval's architecture for enterprise knowledge infrastructure. It weaves together structured data, unstructured documents, regulatory texts, and tribal knowledge into a unified graph that AI agents can query with full contextual awareness, eliminating the dark knowledge gaps that cause production failures.