RAIA

Make trust an evidence based decision.

Review the models behind your AI with performance oversight, explainability and readiness assessments. Make the evidence visible before deciding what comes next.

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RAIA / Explainability StudioProduct interface preview
RAIA Explainability Studio showing feature importance rankings for the golden_regression model.
Understand the model’s drivers

Inspect feature contribution evidence in the real Explainability Studio.

  1. Explainability Studio: feature importance with SHAP
  2. Positive and negative impact counts
  3. Which inputs drive the model’s output, ranked
  1. Performance Oversight: data and concept drift
  2. Reference vs current metrics with threshold colours
  3. Overfitting score
  1. The asset under review, its version and environment
  2. Responsible AI scorecard against target thresholds
  3. Evaluator breakdown: performance, bias, explainability
  1. Register an AI asset
  2. Filter by type, environment, team and owner
  3. Each registered model or agent with owner, team and last run
1 / 4Captured 25 September 2026

Explore the capabilities

What RAIA brings to the work.

RAIA makes model assessment reviewable: an asset registry, performance oversight, bias and explainability analysis, and a readiness evaluation. The tour uses an existing demonstration assessment, including its actual “Not Ready” result.

Discover AI assets

Use the asset registry to locate models and datasets and establish the object of an assessment.

Review performance

Inspect evaluation metrics and model behavior through Performance Oversight. Interpret results in the context of the data and task being evaluated.

Understand model explanations

Use Explainability Studio to inspect feature contributions and understand which inputs influence a model’s output.

Assess bias and readiness

Review assurance evidence across performance, fairness and readiness dimensions. Investigate assessment gaps instead of treating a single score as a guarantee.

Built around your team

Who RAIA is for.

For model owners, risk teams and reviewers who need to understand AI behavior and inspect evidence behind readiness decisions.

A practical place to start

An example starting workflow.

Select an AI asset, inspect its evaluation and explanation views, and review gaps in its readiness assessment before taking action.

See the five step process

Your first digital employee starts with a job to be done

What role would change your business?

Bring us the workflow, capacity gap, risk, or business outcome you need to address. DEOS will help define the role and accountability structure.