Edgerunners
AI Evaluation & Governance

Measure AI Before You Depend on It

AI systems must be evaluated against the work they are expected to perform, the data they use, and the consequences of failure.

We develop evaluation and governance mechanisms that help organizations understand AI quality, reliability, limitations, and operational risks before and after deployment.

What we evaluate

Six dimensions of AI you can depend on

  • Output Quality

    Assess correctness, completeness, relevance, formatting, and compliance with task requirements.

  • Retrieval & Evidence

    Measure whether the system retrieves relevant information and supports its responses with appropriate sources.

  • Tool Execution

    Test whether tools are invoked correctly, parameters are valid, and resulting actions match the intended operation.

  • Workflow Reliability

    Evaluate task completion, recovery, retries, failure modes, and consistency across repeated runs.

  • Security & Permissions

    Test access boundaries, data isolation, authorization rules, and resistance to unauthorized operations.

  • Cost & Latency

    Measure operating cost and response time against application-specific service requirements.

From evaluation to continuous improvement

A loop, not a one-off test

  1. Establish test cases

    Establish representative test cases.

  2. Compare to a baseline

    Compare results against a baseline.

  3. Change deliberately

    Introduce controlled changes.

  4. Watch for regressions

    Monitor for regressions.

For systems operating in consequential environments, define explicit escalation and approval policies appropriate to the level of risk.

Explore the AI Control Plane

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Related capabilities

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Know What Your AI Can Reliably Do

Bring us the task your AI performs and what failure would cost. We will help you define how to measure it.