Evidence-governed decision-making for physical assets
Do we really spend months and hundreds of thousands proving this out, or can we already see the weak spots and what has to be true before we start?
NowBefore money moves: what capital can move now, and what must wait.
Before you fund it
Evidence-governed decision-making for physical assets is the discipline of deciding what deserves action, what to fix, fund, or defer across the operations you run, before effort and capital move, and stating what would make that decision wrong. A wrong question can survive thousands of correct calculations, so we test the decision itself before effort and capital move — and we don’t let a claim stand unless the evidence is there to back it.
The public record
In every one, the evidence existed before the decision did. Governing that gap is the discipline.
We govern decisions like these. 18 capital decisions reviewed and published in full, 1 where we declined to recommend, every read graded on 4 evidence levels. The public case library →
Audits observe. Dashboards display. AI copilots explain. None of them decides whether the evidence is strong enough to act on.
A confident answer isn't a proven one. The decision stays yours — what changes is that you know whether the evidence behind it holds up, before the money moves.
The decision. The assumptions. The weak points. The validation path. The cost of being wrong.
An Operational Decision Stress Test won't promise a better decision — no honest instrument can. What it holds to is narrower and harder: evidence, assumptions and uncertainty are named apart, never blended, before significant resources are committed.
Weeks of review become a decision posture in days, traceable, not trusted.
See it on Ironclad Castings: what the evidence held, what it blocked, and the decision.
See it on a live case →Same method, three moments where effort, engineering and capital move faster than the evidence behind them.
Bidding on an asset before the asset is understood. The clock is moving faster than the evidence.
A governed read before the offer: what's solid, what's a guess, what to verify before resources commit.
Whether to bid, at what number, and on what conditions.
Grounded in real evidence
Not just standards. A read reconciles the asset's own data, its contracts and economics, the climate it runs in, and the codes it answers to, against references that carry authority like ASHRAE, IPMVP, DOE, EPA and NREL.
Making all of it agree, deterministically and traceably, is the hard part, and most of the work. None of it is taken on faith.
See the evidence foundations →The loop
When the world moves
the system runs again.
The first review protects one decision. After that, the system keeps governing the asset as the evidence changes.
Where this is going
The engine that governs a capital decision today is becoming the standard that decision is recorded against, and the layer that governs what any analysis is allowed to justify.
A signed, versioned, tamper-evident record of every governed decision: the evidence, the rival hypotheses, the claims admitted and the claims blocked, the admissible action, and who owned the decision.
Request early access →The same engine governs a decision whatever produced the analysis, your team or a model. As AI floods you with confident answers, it governs what you are allowed to act on.
FAQ
Evidence-governed decision-making for physical assets is the discipline of deciding what deserves action, what to fix, fund, or defer across the operations you run, before effort and capital move, and stating what would make that decision wrong.
Audits observe, dashboards display, and AI copilots explain — but none of them decides whether the evidence is strong enough to act on. A confident answer isn't a proven one; the decision stays yours, and what changes is whether the evidence behind it holds up before the money moves.
The decision, the assumptions it rests on, the weak points, the validation path, and the cost of being wrong.
No, not until the evidence earns it. A preliminary read is not allowed to claim ROI, payback or savings.
The operations you run: manufacturing plants, commercial real estate, energy and midstream, data centers, cold chain, and healthcare. Same method, any sector.
Days, not weeks.
A go / no-go you can defend with its admissibility state, the dominant misframing of whether you are acting on the right variable, a decision risk map, a capital-at-risk range, the rival scenarios still in play, and the cheapest next evidence that would change the decision.
A consultant reasons deeply across the same layers, and that work is valuable; it is also slow, costly, and hard to standardize or trace. A governed read runs the decision through fixed gates, states whether each claim is admissible, and stays traceable to its evidence, in days, not weeks.
An AI can produce a recommendation. It cannot decide whether the recommendation is admissible. That judgment is the work: evidence weighed against fixed gates, with a read that refuses to commit when the evidence has not earned it. A licensed professional certifies it when you need to put it in front of an institution.
Not one source. It weighs the asset's own data, including sensors, telemetry and digital twins, its contracts and unit economics, the climate it runs in, and the rules it answers to, against references that carry authority such as ASHRAE, IPMVP, DOE, EPA and NREL, and says where the evidence is thin.