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Architecture & governance

How K-AI cleans and activates your documents

The K-AI platform: six principles, a data ↔ document mapping, five roles, a five-layer architecture, a market positioning. Read it all, or jump via the table of contents.

The six founding principles

A discipline, not a tool.

01
Document-as-a-Product
A document is not a file in a folder. It’s a product owned by someone, serving a purpose, committing to a quality.
— inherited from Data Mesh
02
Clean before consume
No consumer — human or AI — should consume an unaudited document. A continuous discipline, not a one-off step.
— no consumption without audit
03
Semantics before syntax
Quality isn’t measured in format. It’s measured in meaning: coherence, freshness, completeness, non-contradiction.
— meaning, not format
04
Federated governance
Ownership is business-side (domain Director); the standard is transverse (Document Authority — CDO). That’s what makes adoption possible.
— business + standard
05
Continuous observability
Quality is a flow, not a state. The platform continuously surveys: conflicts, missing subjects, orphan documents, unanswered queries.
— a flow, not a state
06
AI-Ready by design
Documents directly consumable by AI agents via MCP, mirrored ACLs, traced lineage, known freshness. Not a feature — an architecture.
— built for agents
The data ↔ document mapping

Carry over the proven structured-data playbook. Transpose it to documents.

Each document concept inherits the intuition of its data equivalent, and adds the document specificity: quality is measured in meaning, not format.

Structured data world
Document world (DKP)
Document specificity
Data Warehouse / Lake
Document Repository
Document estate seen as a governed asset, not storage.
Data Lakehouse
Document Knowledge Base
Repository + active semantic layer queryable by AI.
Data Product
Document Product
Coherent document bundle packaged as a product with quality SLA.
Data Catalog
Document Catalog
Live inventory with metadata, ownership, quality, ACLs.
Data Lineage
Document Lineage
Trace origin, versions, transformations — critical for AI Act.
Data Quality
Document Quality
Coherence, non-contradiction, freshness, semantic dedup.
Data Steward
Document Steward
Knowledge Manager animating day-to-day. Main K-AI Audit user.
Data Owner
Document Owner
Business Director owning a Document Product. One Owner per Product.
Federated Governance
Document Authority
CDO teams. Carries the transverse standard — owns no Product.
Data Mesh
Document Mesh
Federated governance by business domains, under common standard.
Semantic Layer
Semantic Document Layer
K-AI Neural Semantic Graph — inter-document graph.
Data Observability
Document Observability
Real-time surveillance: conflicts, missing subjects, obsolescence.
The five roles

Who decides? Who produces? Who consumes? Who runs the show?

Ownership is distributed across business domains. Governance is transverse. That’s what makes the model operational.

↘ Click a role to see its responsibilities.

Role 03 / 05

Document Steward

Knowledge Manager in a domain, or extended Data Steward

Animates Producers day-to-day, tracks quality KPIs, triggers reviews, escalates. Spends the most time in the platform.

Responsibilities

Animation, KPI tracking, escalation

Tools

K-AI Audit (daily), quality dashboards

Time spent

High (daily)

How many

1 / domain

The DKP industrializes what you already do manually: detect contradictions, identify missing subjects, run arbitrations. You become orchestrator instead of operator. Your SMEs don’t live in the platform — you do, and you gain leverage.

See this role’s questions in the FAQ →
Architecture in five layers

From source to consumer, through meaning.

Five conceptual layers, aligned with the three capabilities: govern, clean, activate.

01
Sources
The existing document estate
02
Ingestion & indexing
Technical pipelines, run by the Document Engineer
03
Semantic Document Layer
The semantic core — K-AI Neural Semantic Graph
04
Governance & Quality
Operated by Authority / Owner / Steward / Producer
05
Exposure & consumption
Governed access layer (ACL, lineage)
03 — Layer

Semantic Document Layer

The DKP’s distinctive layer. Unified semantic representation that detects subtle contradictions — something no RAG, ECM or Data Catalog can.

Neural Semantic Graphproprietary
Unified semantic graph

Documents, concepts, subjects, actors, dependencies linked

Contradiction detection

Even between documents that don’t reference each other

Subjects & meaning clusters

Including expected subjects not yet covered (missing)

Contextual understanding

Grounded in an explicit representation of relations

Market positioning

The empty quadrant.

No existing category combines strong document specificity with full governance cycle coverage. That’s the opportunity the DKP occupies.

How K-AI is used day to day

Audit, Platform, MCP — see the product demo on the home page.

See the demo →