Graph Databases as Single Sources of Truth for AI
Context
Previously, in the article Volgende stap in AI-maturity: van Experimenten naar Integratie, we explored the importance of centralising knowledge and information in an interconnected, accessible environment not only for the benefit of your organisation but also, and most importantly, to enable AI systems connected to your data to perform effectively.
Without the consolidation and centralisation of information, organisations risk making critical decisions based on incomplete or outdated data, for instance, choosing vendors based on last year’s pricing, planning strategies using obsolete market information, or, as illustrated below, building trains too large for the country’s tunnels.
These same risks apply to AI: lacking a centralised, well-structured knowledge base increases the likelihood of hallucinations and directly reduces the quality of AI outputs.
Indirectly, organisations also incur integration costs when extracting data from siloed systems, as well as procurement costs from maintaining multiple disconnected platforms while attempting to build or integrate AI solutions on top of them.
What is the solution? Centralise and interconnect your data through a graph database.
Knowledge Graph
A knowledge graph (or graph database) is a smart way to organise information so that people, computers, or AI can easily understand relationships between things. It’s like a map of knowledge that connects data points together in a meaningful way.
Instead of just storing data in lists or tables, a knowledge graph connects people, places, companies, products, concepts, processes –you name it, in a web of relationships much like how your brain connects ideas.
Benefits
1. Unified and connected view of all data
Most organisations suffer from data or system fragmentation in silos (CRM data, spreadsheets, customer logs, etc). A knowledge graph enables the connection of all these silos into a single, unified view of information where different systems speak to each other through shared entities and relationships, and teams no longer waste time reconciling.
Success stories: JPMorgan leverages knowledge graphs for risk assessment, fraud detection, investment advice, and supply chain analysis. The bank developed JEL (JPMorgan Entity Linking), a neural entity linking model that connects company mentions in financial news to entities in their knowledge graph, enabling real-time alerts for financial analysts.
2. Foundation for Generative AI and Assistants
Generative AI (like chatbots or copilots) often “hallucinates” or gives wrong answers if it doesn’t have verified data.
When combined with knowledge graphs, AI assistants can ground their answers in verified, structured organisational knowledge.
- Results are accurate, consistent and explainable
- Supports conversational query of internal data
Success stories: Klarna replaced over 1,200 SaaS applications with a knowledge graph, creating the AI assistant “Kiki” that answers 2,000 employee questions daily. The company achieved 85% employee adoption, with some departments reaching 93% usage rates. This transformation enabled Klarna to consolidate fragmented data and dramatically improve productivity.
3. Faster, More Reliable Decision-Making
Business questions often require gathering data from many different systems, leading to delays or inconsistent reports.
Because all data points are connected, insights can be generated faster and with greater confidence.
- Reduces manual data integration and cleansing
- Enables real-time analytics and decision support
Success stories: Bloomberg built the Bloomberg Knowledge Graph, a graph-centric representation of entities and relationships in the financial world that connects cross-domain data from various sources. The graph enriches news stories, supports financial analytics centered around entities, and helps predict how breaking business news will move markets.
Implementation
Building a knowledge graph isn’t just a data project, it’s a strategic initiative that connects business knowledge, people, and technology.
Here’s how organisations typically approach it successfully:
Start with a clear business objective:
Don’t begin with technology. Begin with why by identifying a specific, high-value problem that a knowledge graph can solve.
Examples:
- “We want a unified view of our customers across systems.”
- “We need to connect operational and network data to improve incident response.”
- “We want our AI assistant to answer questions using verified company data.”
This focus ensures your graph delivers measurable impact from day one — and helps you build internal buy-in.
Map your key data sources and entities:
Identify where your data lives and what it represents. Think of your organisation in terms of entities and relationships:
- Entities: people, customers, products, devices, locations, projects.
- Relationships: who owns what, what depends on what, what interacts with what.
Connect and standardise your data:
Connect your existing systems (CRM, ERP, ticketing, or network tools) into the graph, often using APIs or data pipelines.
- Build and enrich your graph: With the foundations in place, you begin populating the graph with nodes (entities) and edges (relationships). At this point, you can start to visualise how your data is connected.
Enable AI Retrieval and Enrichment:
Once your knowledge graph is established, you can leverage it for intelligence and automation:
- Semantic or GraphQL search
- Predictive modeling
- AI Assistants
Scale:
Don’t try to model your entire organisation from day one. Start with a pilot project (one department, one use case, or one data domain) and show value quickly.
Once you prove its impact, it’s much easier to expand across teams and systems.
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