Why Hybrid Intelligence?

From predictive performance to full explainability and making better decisions, Hybrid Intelligence has many advantages over traditional AI. The benefits of this new approach to AI are wide-ranging and bring impactful improvements to all commercial and technical aspects of any AI application.


Hybrid Intelligence models perform at or above best-in-class traditional AI method metrics on predictive performance.

  • No trade-off between explainability and performance
  • High performance models that are transparent and robust
  • Best in class in fit-for-purpose decisions


Hybrid Intelligence models have a known and observable behaviour which means that they can be treated like any expert system, they can be:

  • Audited with certainty
  • Tested and certified for compliance to requirements and regulations
  • Applied to critical applications that may require the inclusion of fallbacks, guard rails and other safety measures


Transparent models and precise prediction explanations mean:

  • Faster Insights: deeper understanding of the underlying data patterns, correlations, and causal relationships.
  • Trust and Transparency: confidence in system reliability and fairness to users, stakeholders, and regulators.
  • Compliance and Accountability: satisfying legal and ethical requirements in regulated industries by providing auditable and justifiable explanations.


The absolute certainty and real-time availability of query and model explanations enable:

  • Identification, Assessment, and Resolution any non-compliant, unaligned or sub-optimal characteristics of a prediction before it is consumed as a decision
  • Build better decisions by using explanation data to improve predictions in real-time workflows
  • Selectively de-automate using explanations to effectively include humans-in-the-loop (HITL) where needed.


  • Train directly from with in-built, fully integrated data pre-processing
  • Less data: better generalisation reduces the quantity and quality of training data needed
  • Leverage existing knowledge from legacy systems to enhance predictive model training
  • Train once: save time and resources with a training algorithm that trains the optimal model in one training run.


  • Model behaviour is precisely known and explanations are guaranteed to be precise and correct
  • Real-time model activation mapping tools give detailed and live monitoring data for anomaly detection, retraining scheduling, cyberthreat detection
  • Simple and reliable integration into systems using symbolic interfaces