modules
Knowledge Graph Engine
AI reads your documents and automatically maps the relationships between people, products, processes, and decisions — building a navigable knowledge graph that your entire organization can explore and query.
AI reads your documents and automatically maps the relationships between people, products, processes, and decisions — building a navigable knowledge graph that your entire organization can explore and query.
your-documents-build-the-graph.-automatically.
Your Documents Build the Graph. Automatically. As your teams write, AI extracts entities — people, organizations, products, concepts, events, locations, and technologies — and maps them into a navigable graph. No tagging. No manual categorization. The knowledge structure emerges from the work your people are already doing. The engine goes beyond explicit mentions. It discovers implied relationships across documents — connections that no single author would recognize because they span departments, projects, and years of institutional history.
data Viz
Knowledge Graph Capabilities
- Entity Extraction
- Real-time, as documents are written
- Relationship Discovery
- Explicit and implied connections across all content
- Entity Types
- 7 built-in + unlimited custom types
- Graph Scale
- 10M+ entities, 100M+ relationships
- Visualization
- Interactive 2D and 3D graph exploration
- Querying
- Natural language and structured graph queries
- Validation
- Semantic consistency checking across the entire graph
What the Knowledge Graph Gives You
subtitle: Transform years of documents into a navigable map of your organization's collective intelligence
Zero-Effort Extraction
desc: Entities are identified and linked in real-time as documents are created. No batch jobs, no manual tagging, no extra work for your teams.
Visual Exploration
desc: Navigate your organization's knowledge as an interactive graph. Filter by entity type, time range, department, or project. Find paths between any two concepts.
Semantic Integrity
desc: Automated validation ensures your knowledge graph stays consistent as it grows — flagging contradictions, duplicates, and gaps.
Knowledge Discovery Rate
value: 340
timeframe: 6 months
unit: percent increase
Cross-Document Connections
value: 12500
timeframe: per quarter
unit: auto-discovered links
Graph Scale Capacity
value: 10000000
timeframe: nodes
unit: with 100M+ edges
content section
value: Your teams are writing documents full of valuable relationships — between people, products, decisions, and systems — but those connections are invisible. MUKR's Knowledge Graph Engine reads your documents as they're written and automatically maps who is connected to what, which decisions affect which products, and where critical relationships exist that nobody knew about.
features
ontology: ATHENA
inference: fullSET
extraction: Prometheus NLP
Section 1
Person
Section 2
Organization
Section 3
Product
Section 4
Concept
Section 5
Event
Section 6
Location
Section 7
Technology
Relationships
products
industries
capabilities
sub modules
use cases
- Epistemological Knowledge
- Knowledge Graphs
business sizes
- Enterprise
- Government
- Mid-Market