Bringing evaluation science into management.
Selection, deployment, compliance, budget — behind every major enterprise decision about AI sits a measurement. We turn the institute's methodology into governance capability you can use directly.
An old law of management: what you cannot measure, you cannot manage.
Three ways in.
Model selection & procurement
Answer on your real business data: which model, is it worth it, and where the risks are.
- Business construct definition & task sampling
- Multi-model comparison with uncertainty reporting
- Cost–capability–latency decision maps
- Evidence checks on vendor claims
Private benchmarks & continuous evaluation
A ruler for your core business that cannot be gamed — and evolves with the business.
- Leakage-proof item design & rotation
- Validity and reliability verification
- Continuous monitoring from pre-launch into production
- Versioned maintenance
AI governance systems
Wire evaluation into management: who measures, how, who sees the results, who is accountable.
- Evaluation roles & tiered authorization
- Audit trails & evidence retention
- Mapping compliance requirements to evaluation protocols
- Board-level AI risk reporting
Different problems, one methodology.
Define the construct
Say precisely what is being measured, and why it relates to business outcomes.
Design the instrument
Tasks, item banks, protocols — and leakage protection.
Collect the evidence
Reproducible evaluation runs, with uncertainty and significance.
Wire it into management
Reporting, roles, audit, and continuous monitoring.
Written for three people.
The technology leader
You need a selection rationale you can defend to the CEO and the board.
The business owner
You need to know the real gain AI delivers — not the gain in the demo.
Risk & compliance
You need an evidence chain that stands up to regulators and auditors.
Start with thirty minutes.
We listen to the problem before talking method. Bring one real evaluation puzzle.
hello@aims-lab.org