Evaluation & assurance6 MIN READ
How to design a joint AI evaluation lab
A joint lab should be a governed way to create decision evidence across organisations, with shared methods but explicit ownership of systems, data and conclusions.
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Partnerships
Joint working models for agent delivery, research and assurance ecosystems.
In Partnerships
7 articlesGovernance & privacy6 MIN READ
How to collaborate when client data cannot leave the environment
Keep sensitive data under client control by moving approved methods and people to the environment, exporting only the minimum reviewed evidence needed for collaboration.
Impact measurement6 MIN READ
What conference participation proves—and what it does not
Attendance, speaking and sponsorship are evidence of activity; useful conference claims require separate measures of learning, relationships, follow-through and resulting decisions.
Strategy & transformation6 MIN READ
How to qualify an AI technology partner before designing a joint offer
Qualify a technology partner through evidence of fit, controls, delivery behaviour and commercial alignment before combining brands or promising a joint service.
Evaluation & assurance6 MIN READ
What makes an AI assurance partnership useful?
A useful assurance partner improves the quality of a specific AI decision while leaving ownership, evidence and operational accountability with the organisation.
Strategy & transformation6 MIN READ
Vendor-neutral does not mean anti-vendor
Neutral advice compares dependencies and evidence against the client's decision; it does not pretend every platform is interchangeable or that vendors add no value.
Governance & privacy6 MIN READ
The responsibility map every AI partnership needs
A useful responsibility map connects each AI decision and artefact to a named accountable owner, a performing role, required contributors and evidence of completion.
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