The Challenge with Traditional Knowledge Graphs
Most traditional Knowledge Graphs behave like very smart relational databases. Before you can store anything, you need to know the entire structure of the world - entity types, relationship types, properties. For messy, unstructured content like PDFs, emails or notes - that’s almost impossible in practice.
Vertical Knowledge Platform's Vector-Based Solution
Vertical Knowledge Platform goes the other way - to a vector-based graph built on embeddings instead of a rigid schema.
In practice, this means:
- •No upfront schema - instantly start building
- •Automatic relationships - discovered via vector similarity
- •Native support for unstructured text - documents, notes and articles work out of the box
- •Natural LLM integration - simple REST API with semantic search
- •Continuous uncertainty - similarity scores from 0.0–1.0
- •Full flexibility - the same engine works across any domain and any type of content.
Vertical Knowledge is like a library where you just throw in the books, and an AI librarian reads everything and instantly finds what you need when you ask. With $VERTAI, your docs become a live knowledge graph - with less setup and more control.