Responsible AI Evaluation

Beyond model capability, toward governance

KalpaneAI helps organisations evaluate large language models and GenAI systems against Dutch and EU expectations across compliance, privacy, reliability, fairness, transparency, and human oversight.

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KRAI — KalpaneAI Responsible AI Index

A structured evaluation framework for comparing LLMs through a governance lens, not just a capability lens.

KalpaneAI Responsible AI Index framework
Leader

Trusted
Benchmark

Strong alignment across compliance, privacy, robustness, fairness, and transparency dimensions.

Compliant

Deployment Ready

Meets key Responsible AI expectations with manageable limitations and acceptable governance posture.

Moderate

Use With Caution

Suitable for limited use cases but requires stronger controls, monitoring, and human oversight.

High Risk

Significant Concerns

Material issues across one or more evaluation dimensions that may affect safe enterprise adoption.

Critical Risk

Not Fit for Deployment

Serious compliance, reliability, or governance gaps that create unacceptable risk under EU or Dutch expectations.

Moderate Tier

Models suitable for bounded use cases, but requiring higher caution and stronger safeguards.

*Ratings shown below are illustrative examples for demonstration purposes only and do not represent official or validated benchmark results.
Score Range: 6.0 – 6.9

Evaluation dimensions

  • Legal compliance under EU AI Act and AP guidance
  • Privacy and data protection alignment with GDPR
  • Accuracy, robustness, and hallucination control
  • Bias, non-discrimination, and fairness testing
  • Transparency, disclosure, and AI limitations

Business relevance

  • Creates a practical risk posture for enterprise AI adoption
  • Supports safer selection of LLMs and GenAI tools
  • Connects technical evaluation with regulatory accountability
  • Helps boards and delivery teams speak a common governance language

How KalpaneAI Evaluates

KRAI connects market-facing GenAI systems with structured testing and governance logic.

Legal & policy review

Evaluate model and system posture against EU AI Act, AP expectations, and deployment context.

Privacy & data protection

Assess governance, data handling, retention patterns, traceability, and privacy control expectations.

Reliability & robustness

Test hallucination exposure, response consistency, and operational boundaries in enterprise workflows.

Transparency & disclosure

Measure how clearly the system communicates identity, limitations, and usage boundaries.

Why Responsible AI Starts Before Deployment

Purpose before technology leads to cleaner, more accountable AI systems. The model matters, but the controls, boundaries, and oversight surrounding the model matter even more.

Purpose

Start with a clear use case and necessity before adding GenAI.

Data

Build on proper data governance, privacy controls, and lawful processing.

Model

Understand robustness, limitations, and update behaviour.

System

Assess supplier dependency, observability, and traceability.

Human oversight

Ensure meaningful review, escalation, and accountable use.

Discuss Responsible AI

Need an evaluation lens for LLM selection, AI governance, or Dutch and EU Responsible AI readiness?

Email KalpaneAI