Private AI evaluation
Measuring an AI system's real value to your business using evaluations you own and control: your data, your criteria, your infrastructure, kept invisible to the vendors being evaluated. You produce the number; no vendor can see or game it.
Owned evaluation
An evaluation the customer owns outright: the golden datasets, the harness, the methodology, and the result belong to the enterprise and run inside its perimeter, rather than being rented from or produced by an AI vendor.
Golden dataset
A curated set of examples that captures what a good outcome looks like for a specific task, used as the reference an AI system is scored against. Golden datasets encode a business's own standard of quality, which makes them proprietary.
Ground truth
The trusted, correct answers an evaluation measures against. Without ground truth built on your own data, an AI score is only a comparison to the vendor's assumptions, not to reality.
Eval harness
The tooling that runs an AI system against a dataset, applies scoring criteria, and records results. An owned harness lets a team re-run evaluations continuously, including on every model swap.
Benchmark contamination
When test data leaks into a model's training set, inflating its benchmark scores without improving real capability. It is one reason public benchmarks decay: what is measurable and public gets trained against.
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Goodhart's Law
The principle that when a measure becomes a target, it stops being a good measure. Applied to AI: once a vendor knows the metric you care about, the metric gets optimized to look good rather than to reflect value.
AI ROI
The measurable return an AI system delivers to the business, in cost saved, revenue influenced, hours returned, or risk reduced, net of a credible counterfactual. A defensible AI ROI number is measured on your data and owned by you.
Data readiness
The state of enterprise data being structured, governed, and evaluable enough for an AI system to act on and for outcomes to be measured. Most enterprise data is not AI-ready, which is why measurement fails before it starts.
Build, operate, transfer
An engagement model where a partner builds a capability inside the customer's perimeter, optionally operates it as a bridge, then transfers it to the customer's team to own and run. The end state is customer ownership.
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Evaluation criteria
The definition of what counts as good for a given task: the outcomes, thresholds, and edge cases used to score an AI system. Because they encode institutional judgment, evaluation criteria are intellectual property.
Private benchmark
A benchmark held privately by an enterprise rather than published, so it cannot be trained against or gamed, and so the criteria stay hidden from the vendors being tested. The opposite of a public leaderboard.