KDG STUDIO
A Dedicated Workbench for Structuring Knowledge
Beyond RAG — We build knowledge systems tailored to your business domain.
KDG STUDIO takes a fundamentally different approach from generic RAG search systems. By building a 4-layer knowledge architecture on a Neo4j graph database, it structures your business processes, decision criteria, and tacit knowledge directly as knowledge assets. From software development traceability analysis to manufacturing value structure visualization, we propose optimal knowledge systems for your domain-specific challenges.
Feature 01
KBMS — Knowledge Base Management System
Just as a DBMS abstracts database mechanisms, KBMS abstracts knowledge storage, retrieval, and relationship management. Built on a Neo4j graph database with a 4-layer knowledge architecture, it serves four AI subsystems: Input, Analysis, Retrieval, and Output.
- 14-layer traceable knowledge management: Original Layer (raw documents) → Formal Layer (structured information) → Insight Layer (tacit knowledge, decision criteria) → Meta Layer (schemas, domain definitions)
- 2Graph structure with nodes (minimum knowledge units) and edges (relationships) enables multi-hop relationship traversal that is impractical with relational databases
- 3Hybrid search combining full-text, semantic (vector embeddings), and graph traversal helps you access relevant knowledge
Feature 02
Knowledge — Knowledge Lifecycle Management
As knowledge is formalized, it grows explosively, making it difficult to distinguish signal from noise. KDG STUDIO controls knowledge expansion while maintaining quality through five mechanisms: Shared Scope, Lifecycle, Consolidation, Views, and Quality Gates.
- 1Shared scope (team → project → domain → system → basic) with search priority ensures the most relevant knowledge surfaces first for each user
- 2Automatic state transitions (Draft → Published → Referenced → Stale → Archived) create a self-cleansing model where unused knowledge naturally sinks while active knowledge rises
- 3Five quality gates (reusable? multi-context applicable? valid after 6 months? requires 2+ sentences? not duplicate?) determine which knowledge warrants formal structuring
Feature 03
Use Case: Large-Scale Software Development Analysis (LRA)
As an application of the knowledge system, LRA (Local Repository Analysis) cross-analyzes software development artifacts. It connects requirement documents, source code, test cases, and GitHub issues as a graph structure, enabling requirement traceability, change impact analysis, and coverage gap detection.
- 1Automatically build multi-stage traceability across Requirements ↔ Design ↔ Code ↔ Tests. Instantly visualize impact scope of requirement changes via graph traversal
- 2Automatically detect gaps: untested requirements, code without requirement traces, and code changes without requirement drivers
- 3Comprehensive artifact ingestion from PDF/Word/Markdown requirement documents, Git repository structure analysis, and GitHub API integration
LRA = Local Repository Analysis. A methodology that directly analyzes target repositories and structures them as knowledge graphs.
Feature 04
Use Case: Manufacturing BOM Cross-Analysis & Value Visualization
Another application is manufacturing BOM (Bill of Materials) cross-analysis. E-BOM (Engineering), M-BOM (Manufacturing), P-BOM (Procurement), S-BOM (Service), Q-BOM (Quote) — five BOMs siloed across departments are connected through knowledge graphs. By introducing R-BOM (Result BOM), it structurally visualizes where your company's added value is created and where it is lost.
- 1LLM-based semantic name reconciliation integrates multiple IDs for the same part (design part numbers, SAP codes, common names) with confidence scoring. Change wave analysis via graph traversal
- 2Gap analysis between Q-BOM (value hypothesis) and R-BOM (actual value) reveals where value is created and lost in each process. Veterans' intuition is accumulated as knowledge and transformed into organizational assets
- 3Five specialized apps (Mapping, Parts, Integrity, ECM, PLM) covering name reconciliation through change management to value structure executive dashboards
Feature 05
Use Case: BPO Business Analysis (Knowledge-Driven Back-Office Automation)
In large corporate groups, back-office operations in accounting, legal, and IT departments span multiple subsidiaries, generating massive volumes of routine and semi-routine tasks. KDG takes a 'knowledge structuring' approach, accumulating veteran staff's decision criteria, company-specific patterns, and historical resolution knowledge in KBMS, combining them with LLM reasoning for knowledge-driven BPO automation.
- 1Intercompany Reconciliation (ICR): 3-layer knowledge model with LLM reasoning delivers end-to-end workflow from CSV import → matching → elimination entries → audit reports across multiple group companies
- 2Expense Processing (EXP): Receipt OCR → policy compliance check → dialogue → approval, with internal policies, tax regulations, and past findings accumulated as knowledge for continuously improving accuracy
- 3SaaS Usage Review (SCC) & Contract Review (CCK): Structure security review and contract review criteria for consistent assessments with auto-generated rationale
Let's design the optimal knowledge system for your business
KDG STUDIO is not a generic tool. We deeply understand your business structure, decision criteria, and tacit knowledge, then build a knowledge system tailored to your domain. Software development traceability and manufacturing value analysis are merely examples. Let us explore together what knowledge structure best addresses your challenges.