Consequence Intelligence
Consequence Intelligence makes the consequences of a decision visible before execution — what can happen next, what can cascade, what becomes irreversible, and which options are gained or lost.
We do not predict the future. We make consequences visible.
Definition
Consequence Intelligence is the discipline and infrastructure layer that evaluates, before a decision is executed, how a given intent or action changes what can happen next — which outcomes become reachable, how likely each one becomes, which effects cascade through dependent parts of a system, which commitments become difficult or impossible to reverse, and which future options are gained, narrowed, or lost. It does not predict a single future. It makes the structure of a decision's consequences visible, with uncertainty stated rather than hidden.
How it differs from reporting and forecasting
Business intelligence describes what has already happened. A forecast tries to name what will happen. Consequence Intelligence does neither: it takes a decision that has not yet been executed and examines the structure of what it would set in motion — the range of plausible outcomes, the dependencies that carry an effect further, and the commitments that cannot be taken back.
A practical example
This example is illustrative, not a customer case. A startup founder is considering a senior hire. A consequence view of that decision asks which assumptions the hire depends on (for example, that revenue grows as planned), how much runway remains if those assumptions fail, which commitments become hard to reverse (compensation, equity, reporting lines), and what other parts of the company the choice constrains. The output is a structured map of those links, with uncertainty stated, so the founder can see what the decision commits them to before committing.
Where Capital Firewall fits
Capital Firewall is the first product built on this approach, from Passage Holding. It applies the approach to one specific decision at a time and returns a structured analysis: confirmed facts, named assumptions, unknown dependencies, consequence chains, failure modes, and reversal conditions. It is built on Monte Carlo simulation and Markov modeling. The working application is the Decision Lab. The approach and the product were developed by Heorhii Hohilauri, also known as George Gogilayri.
Limits, and what exists today
- Today: Capital Firewall is in beta with early access. It is the only product that applies the approach.
- Not a prediction. Outputs are structured consequence analyses based on the context a user provides. They are not forecasts, recommendations, or financial, legal, investment, or tax advice. The decision stays with the user.
- Not validated. The underlying methodology is a research program in development. It has not been empirically validated, and no result on this site should be read as validation.
- A name, not an industry standard. "Consequence Intelligence" is the name Passage Holding uses for this category. We do not claim it is a widely recognised or established term.
- Later, not now: a broader engine and infrastructure layer is a long-term direction. It is not available, and no launch dates are committed.