Ontology-based AI Knowledge Management
An ontology-based AI knowledge management solution that links the documents and data scattered across your organization with meaningful relationships, turning them into a knowledge asset anyone can revisit and reuse.
Organize information across your organization into a knowledge graph
Parse documents in Korean and other formats, automatically extract entities (documents, owners, projects, schedules, and more) and their relationships, and build a knowledge graph ready for decisions and execution.
Clients & Partners
Vault Ontology Solution
Vault is not a single capability but a workflow that combines structure parsing, graphs, permissions, and knowledge-state management. It also supports on-premise and network-isolated deployment.
Workflow-based DB Architecture & Integration
Design real-time integration with unstructured data (documents, technical materials), HR systems, ERP, approval systems, and more.
Execution-focused Platform & Custom Dashboards
Support decision-making and execution within the platform, while identifying expired or stale knowledge so it stays current.
AI-optimized System Architecture
Leverage centrally managed data and the knowledge graph to plug AI search, analytics, and reasoning into a wide range of use cases.
Vault Key Features
Multi-format Parsing (incl. HWP)
- Parse and embed unstructured documents in formats such as HWP, Word, PPT, PDF, and images, and link them into the knowledge graph.
- An LLM summarizes each uploaded file; later files are compared via RAG against existing summaries and clustered into the same entity when they match.
Automatic Entity & Relationship Extraction
- The LLM automatically extracts core business entities—documents, people, projects, decisions, schedules—and the relationships between them.
- Extracted entities and edges are applied only after user approval, and can also be defined and edited manually when needed.
Schema-graph Relationship Modeling
- Define and visualize relationships between entities on a DB-ERD-style 'Relation' page.
- Edges are first-class objects with named types, so multiple relationships (e.g. 'Owner', 'Author') between the same two entities are expressed naturally.
Instance Graph View
- Select a data row in the list view to visualize that instance and its connections to other instances as a graph.
- Pin frequently used entities to the LNB favorites bar to jump straight into the relationship and list views.
Knowledge-state Management
- Automatically identify expired or stale knowledge and surface it via dashboards and related-entity analysis so it stays current.
- Deleting an entity or relationship definition does not cascade — existing data is preserved as nodes with detached edges.
Permissions & Secure Deployment
- Set fine-grained access permissions on entities and relationships by organization or project.
- Supports on-premise and network-isolated deployment to meet enterprise, public-sector, and defense security requirements.
Use Knowledge via AI Chat
- Ask natural-language questions and get answers grounded in the knowledge graph through the chat interface.
- Export data in standard formats such as JSON/RDF, keeping a clear migration path to other ontology systems.
Talk to us about Vault
Work with Lattice experts to design how Vault fits your organization and turn scattered documents into a usable knowledge asset.