Who runs test management in your AI project?

AI initiatives need test management that understands evaluation dimensions and walks stakeholders through non-determinism, drift and bias questions. We take on the role operationally — as an interim mandate, project support or a time-boxed additional resource.

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Why this offering

Four reasons for experienced AI test management.

AI context first-hand

Evaluation dimensions, drift, hallucinations, bias as concrete decision points in the daily project, not just buzzwords.

Stakeholder translation

Business, development, compliance and management are brought together at eye level. Decisions are made where they belong.

Model-independent

Independent of LLM stack, ML framework or vendor. The methodology carries through the project and across model generations.

Linked with project management

Test streams run in sync with development and release cycles. Escalation upwards and coordination downwards from one source.

What you get

Six building blocks of an AI test management role.

// 01

Test strategy for AI products

Risk-driven test approach, fitting evaluation dimensions, clear gate criteria for the release decision.

// 02

Test data organisation

Build-up and maintenance of golden datasets, edge cases and adversarial sets across the project. Versioning and traceability.

// 03

Stakeholder steering

Translation between business, development and compliance. Decision paths visible, risks transparent early.

// 04

Test coordination

Planning, prioritisation and tracking across multiple test streams. Aligned with development and release cycles.

// 05

Release preparation

Evaluation summary, risk assessment, documented go-live recommendation with clear exclusion grounds.

// 06

Drift monitoring handover

Hand-off after release to operations teams, with a clear handover plan for monitoring and re-evaluation.

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Method toolkit

What we work with.

Interim test manager

Day-to-day role inside the project. Steering, coordination and stakeholder work.

AI test concept template

Structure for evaluation dimensions, gate criteria and reporting paths.

Evaluation dashboards

Visibility on evaluation progress, thresholds and release readiness.

Stakeholder reporting

Role-appropriate formats for team, product and management.

Release gate design

Decision criteria, escalation paths, documented sign-offs.

Network placement

Additional resources from the QCT network when needed and capacity is tight.

Tool stack

What we use in our test setup for AI projects.

A proven blend of classical test-management tools, AI test automation, and eval frameworks for LLM outputs.

Questions

What we are often asked.

How does your test management differ from the classic kind?

Classic test management checks functionality against a specification. AI test management works with evaluation dimensions, thresholds and confidence levels. Stakeholder communication and release documentation change accordingly.

Can you take on partial roles only?

Yes. Typical cuts: only test concept creation, only evaluation planning, only release preparation or only stakeholder reporting. The cut is agreed with you.

How does this fit with your "Testing AI" consulting?

The consulting works out the test concept together with your team. The service then takes on the operational steering — useful when your team does not have the capacity or the role is to be deliberately externalised.

Test management that understands AI projects.

Interim mandate, project support or a time-boxed additional resource — experienced steering for your AI initiative.