Designing Data Context AI Can Understand
Why encore Ontology Engineering Consulting Pack
AI needs structured data to deliver accurate analysis, recommendations, and predictions.
Ontology Engineering Consulting Pack connects data through ontology, creating a semantic structure AI can understand.
encore Ontology Engineering Pack covers data analysis, ontology modeling,
and data construction to deliver customer value through AI Agents and graph-based analysis.
Consulting That Reduces Time and Cost
The pack builds ontology and graph structures from customer data.
Problem-specific analysis templates enable rapid insight.
- Project
Templatization - AI-Powered Consulting Automation
- Advanced Prompt Engineering
- Maximized
Consulting Quality - Shorter
Delivery Time
Architecture of KNIT
encore’s ontology consulting pack has three modules: modeling, data build, and insight.
It designs standard-model ontologies around customer pain points
and supports the full build process with workflow templates.
- Modeling
Module - Data Build
Module - Insight
Module
- Ontology model design
- Data collection, structuring, and validation
- Value creation
Processes of KNIT
Connect data analysis to business value in one pack.
- 01 Customer Data Analysis
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- Prepare and process data with AI Agents
- 02 Ontology Modeling
-
- Transform customer data into ontologies with domain- and problem-specific AI Agents
- Core ontology modeling (5W1H)
- Standard ontology
modeling - Applied ontology
modeling
- 03 Ontology Data Build
-
- Build customer data into customer-specific ontologies with AI Agents
- 04 Customer Value Creation
-
- Use AI Agents and algorithms to solve customer problems with customer data
3 Core Models of KNIT
01Modeling Module
Design consistent, scalable ontology models for each domain and challenge.
The Modeling Module is an AI module that designs standard and applied ontology models based on encore’s core ontology model.
02Data Build Module
Understand data meaning and build ontology structures.
The Data Build Module is an intelligent module that consistently structures and validates data with encore ontology models.
Beyond ETL, it combines data literacy-based analysis, multidimensional ontology modeling,
and performance-optimized data pipelines for large-scale environments.
-
01
Data Literacy-Based Collection and Preprocessing
- Intelligent preprocessing that understands and structures data, not just cleans it
-
02
Multidimensional Ontology-Based Data Modeling
- Intelligently connect cleaned data to ontology structures
-
03
Performance-Aware Data Pipeline Design and Build
- Build data in defined ontology-based structures
- Optimize pipelines for stable operation at scale
-
04
Ontology Model Validation
- Verify consistency between built data and ontology models
03Insight Module
Create business value from semantically structured data.
The Insight Module enables people and AI to reason over semantically structured data and create value with encore ontology models.
- Intelligence That Turns Analysis into Decisions
People and AI reason together on ontology-based data to create value.
-
01
Root Cause Tracing
Provenance Tracing
Early risk detection,
cause and propagation path analysis -
02
Pattern Analysis
Pattern Analysis
Repeated structures, success/failure patterns,
and causal relationships -
03
Recommendations
Recommendation
Explainable, meaning-based
decision options -
04
Diagnosis & Prediction
Diagnosis & Prediction
Current-state assessment,
future scenario prediction
Features of KNIT
Built on deep data expertise, graph database research, GDB experience, and graph project delivery.
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Template-Based AI Agent Automation
- Use prevalidated industry ontologies as templates to semi-automate processes with AI Agents.
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Data Modeling Expertise
- Design ontology structures for business requirements.
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Large-Scale Performance
- 25+ billion-record tuning with SQL/Cypher expertise.
Use Cases of KNIT
Can your data be structured for AI?
Diagnose data relationships and context with the encore Ontology Engineering Pack.
-
Security and Threat Root-Cause Tracing
- Build ontologies from event and network logs to trace attack causes.
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Company Similarity Analysis
- Find companies similar to a target company from multiple perspectives.
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Supply Chain Risk Analysis
- Apply to risks requiring relationship and pattern analysis.
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Contract and Transaction Pattern Analysis
- Apply to work requiring relationship structure analysis.
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AI Agent-Based Process Automation
- Build workflows for collection, preprocessing, ontology creation, graph loading, and use.