Knowledge Graph Demo
Unlock the hidden connections in your data.
What is a Knowledge Graph?
A knowledge graph is a structured representation of real-world entities - people, companies, skills, roles - and the relationships between them. Instead of storing data in isolated tables or documents, a knowledge graph connects every piece of information into a rich, traversable network of nodes and edges.
This structure mirrors how relationships exist in the real world: a person works at a company, holds a set of skills, knows other people, and has held previous roles. By encoding these connections explicitly, a knowledge graph makes it possible to ask questions that span multiple data sources in a single query.
Why Knowledge Graphs?
Retrieve Siloed Data
Enterprise data lives across CRMs, HR systems, spreadsheets, and emails. Knowledge graphs bridge these silos, making information that was previously unreachable discoverable through connected queries.
Empower AI Agents
AI agents can traverse a knowledge graph to follow relationships, reason over multi-hop connections, and surface insights that flat search or vector retrieval alone cannot reach. The graph gives agents a structured map to navigate.
Surface Hidden Relationships
Traditional databases answer questions about individual records. Knowledge graphs reveal patterns across records - shared connections, overlapping skills, mutual contacts - that would otherwise stay invisible.
The Three Demos
Each demo mode highlights a different way that knowledge graph structure creates value beyond conventional search.
Graph Explorer
Search for a person, company, or role and manually expand the network node by node. This demonstrates how interconnected data surfaces relationships that flat keyword search would miss entirely - you can follow the edges to discover who someone knows, where they have worked, and what skills they share with others.
Job Matching
Select a job title and let an AI agent traverse the knowledge graph to find the best-fit candidates. Rather than matching on keywords alone, the agent walks the graph - evaluating career trajectories, shared connections, and overlapping skills - to produce richer, more relevant results than a traditional applicant tracking system.
Networking
Enter a personās name and the agent suggests who they should meet and why. By reasoning over second- and third-degree connections in the graph, the agent discovers non-obvious introductions - people who share mutual contacts, complementary skills, or aligned career paths - that neither party would have found on their own.
Interested in a bespoke solution?
We build custom knowledge graphs tailored to your organisationās data. Get in touch to explore whatās possible.
Contact UsAll data used in this demo is sourced from publicly available datasets. No private or proprietary information is included. All names shown are synthetically generated and do not represent real individuals.