Contextualise AI is a data integration and analytics platform designed for organizations of all kinds. It helps teams bring together data from different systems, understand how it’s connected, and use that data to make better decisions; often supported or enhanced with AI.

Data Integration and Lineage

The Contextualise AI platform allows users to connect and integrate data from many sources, including databases, spreadsheets, APIs, and documents. Once data is ingested, it goes through a series of transformation steps that are:

  • Modular: Each transformation is tracked and reusable.
  • Versioned: Changes to data pipelines are tracked, so users can see how data has changed over time.
  • Auditable: It’s easy to understand where data came from and how it was processed.

This approach gives users trust in the data they’re working with.

Knowledge Graph Structure

Instead of flattening data into tables, the Contextualise AI platform represents it as a graph, a network of connected entities like people, assets, events or documents.

This has a few key benefits:

  • Relationships are first-class citizens: For example, not just “who ordered what,” but “how that order relates to suppliers, shipments, delays, etc.”
  • Flexible schema: Graphs make it easier to evolve the data model over time.
  • Context: Users can see how data points are related and explore those connections directly.

Analysis and AI Integration

The Contextualise AI platform includes tools for both manual and automated analysis:

  • Users can build models in Python or other supported environments, or plug in existing models.
  • AI models can be applied to data directly in the platform; for example, to forecast demand, detect anomalies or classify documents.
  • Human-in-the-loop workflows are supported, meaning that AI suggestions can be reviewed and adjusted by users before being applied.

It is important to note that the Contextualise AI platform doesn’t try to replace humans but helps automate repetitive work and support decision-making.

Applications and Workflows

Organizations use the Contextualise AI platform to create applications on top of their data. These can be dashboards, simulations, alerting systems or operational tools.

Examples include:

  • Political analysis
  • Monitoring supply chains
  • Managing infrastructure maintenance
  • Building digital twins

These applications are often built using a combination of structured data, graph relationships and AI predictions.

Benefits of Combining Knowledge Graphs with AI

Feature Benefit
Graph-based modeling Makes it easier to represent and explore real-world systems and relationships
AI integration Allows predictive models to work directly with contextual data
Data lineage and explainability Makes it easier to trust and audit how results were produced
Modular pipelines Helps teams reuse and adapt work over time

Summary

Contextualise AI helps organizations manage complex data and use it in their operations. It combines a graph-based approach to modeling the world with tools for building data pipelines, applications and AI workflows; an all in one platform. This makes it easier for different teams to work with data and apply it to real problems.