Intelligence Through Causal Analysis

Where Data Points Become Insights

Causal knowledge graphs don't just store data—they reveal the hidden relationships that drive understanding. From predicting disease outbreaks by connecting symptoms, locations, and populations to identifying financial fraud through transaction networks, we build graph intelligence that sees patterns humans miss. This is how WHO predicts pandemics and how banks catch criminals.

Connecting global data to support informed public health decisionsConnecting global data to support informed public health decisions

The Power of Connected Intelligence

Traditional databases store data. Causal knowledge graphs understand it. By mapping relationships between billions of entities—people, places, events, concepts—our graph systems discover insights that save lives, prevent fraud, and predict the future. When WHO needed to predict epidemics, causal knowledge graphs delivered days of advance warning.

Graph Intelligence at Planetary Scale

From connections to predictions

Every pandemic starts with seemingly unconnected events. A symptom here, a travel pattern there, a weather anomaly somewhere else. Our knowledge graphs connect these dots instantly, revealing patterns before they become crises. This isn't just data storage—it's predictive intelligence.

Using EIOS tools to explore complex networks of epidemic intelligence data
Using EIOS tools to explore complex networks of epidemic intelligence data
Lead designer Blanca with EIOS global public health intelligence
Lead designer Blanca with EIOS global public health intelligence

Graph Intelligence Impact

Billions
Relationships

connected and analysed

Days Earlier
Pattern Detection

than traditional methods

Under 100ms
Query Response

across billion-node graphs

70%
Better Predictions

versus traditional models

EIOS: Predicting Pandemics with Graph Intelligence

Building Your Knowledge Graph

1

Domain Understanding

1-2 weeks

2

Data Integration

3-4 weeks

3

Graph Development

4-6 weeks

4

Inference Implementation

2-3 weeks

5

Operationalization

2-3 weeks

Knowledge Graph Questions

Regular databases store data in tables. Knowledge graphs store relationships as first-class citizens, enabling traversal, pattern discovery, and inference that's impossible with traditional databases.

We've built graphs with billions of nodes and edges, queryable in milliseconds. Modern graph databases scale horizontally, handling virtually unlimited size.

Multiple strategies: entity resolution, deduplication, validation rules, confidence scoring, and human-in-the-loop verification for critical connections.

Absolutely. Graphs complement existing databases, drawing data via APIs, ETL, or real-time streams. They enhance, not replace, current infrastructure.

Modern graph databases deliver sub-second queries on billion-node graphs. With proper indexing and caching, performance exceeds traditional databases for relationship queries.

Automated updates, version control, quality monitoring, and continuous enrichment. Graphs evolve with your data, becoming more valuable over time.

Ready to Unlock Hidden Intelligence?

From fraud detection to epidemic prediction, graphs reveal what data hides

Whether you need to understand complex relationships, predict patterns, or discover hidden connections, let's explore how knowledge graphs can transform your data into intelligence.

Knowledge Graphs & Inference - Adappt