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Digital Twins simulate behavior

Empower continuous discovery and innovation
  • Predict how conditions change over time
  • Model dynamic evidence-driven interactions
  • Explore dynamic unbiased options and alternatives

About Causaition

Causaition is a biomimetic AI engine that employs an advanced digital twin ecosystem with a decision analysis workbench containing expert knowledge insights. This multi-disciplinary approach follows a set of principles rather than deterministic code.

Uncover realities invisible to ML/NLP

Causaition Biomimetic AI Engine

Our approach is a paradigm shift, moving from obscuring reality by narrow technological constraints to discovering reality by applying real-world reasoning to real-world data. As Einstein said, “You cannot solve problems using the thinking that caused them.”

Digital Twin EcoSystem

  • Run scenarios
  • Insights and actionable intelligence
  • Predict the future

Knowledge Graphs

  • SME Expert insights
  • Model creation
  • Understand relationships and connections

Industry Workbenches

  • Built with SME input
  • No need for coding
  • Enables dynamic unbiased exploration
Model an object, person, or process
scenario planning
Simulate behavior
Run Why analytics
Explore What if scenarios

Reduce time-to-market pressures, improve quality, and predict lifecycles:

  • develop processes and optimize efficiencies and quality
  • test new treatments and drugs
  • find dark data anomalies and patterns
Organizations can create multiple digital twins to simulate complex relationships between entities. Generate richer behavior insights and optimize:
  • simulations
  • scenario planning
  • decision making
Find hidden knowledge in your dark data.

Digital twins can form what’s known as an enterprise metaverse – a digital and often immersive environment that replicates and connects every aspect of an organization to optimize simulations, scenario planning, and decision-making.

Knowledge Graphs & Industry Workbench

Identify objects and understand the relationship between them

Combine diverse data

Organize knowledge in context and setting

Create knowledge

Causaition’s knowledge graphs support the creation of new knowledge, establishing connections between data points that may not have been apparent previously.

Improved problem analytics

Unbiased exploration of options and alternatives

Full evidence traceability

The workbench takes care of all aspects of data extraction, from processing to model deployments and visualization.

Interested in our Technology?