Measurement
We make the real economics of production AI visible — what each workload actually costs, where the spend goes, and how much of it is recoverable. Measured against what is achievable, not against a number we wish were true.
Capabilities
The instrument layer beneath production AI.
Rather than cost per token, we should be thinking about cost per outcome!
Thompson Kinkade Foundry works within the AI stack, leveraging optimization points across multiple levels between the models themselves, and those who are paying for the service. Our capabilities are organized around a simple sequence — see the truth, then act on it, in environments where both have to be trusted. Our first platform, ArborEx, applies this instrument layer to live AI workloads.
We make the real economics of production AI visible — what each workload actually costs, where the spend goes, and how much of it is recoverable. Measured against what is achievable, not against a number we wish were true.
Measurement is only useful if it changes the bill. We turn what the instruments reveal into the basis for running the same work for materially less — without changing the result the work was supposed to produce.
For government and regulated industry, where the work runs is as important as what it costs. We build for environments that demand provenance, auditability, and control over where data and compute live.
Our discipline
One principle runs through everything we build: never confuse what is measured with what is modelled.
It is easy to make AI economics look good on a slide. It is harder, and far more useful, to show a number you can actually stand behind. We hold a strict line between figures that are observed from real systems and figures that are estimated — and we label which is which, every time.
That honesty is not a constraint on the work. It is the work. The value of an instrument is only as good as your trust in its reading.