Consulting

Matrix

Jalaran Matrix scores options against criteria you weight yourself, then shows which criterion actually drove the answer and how fragile that answer is.

Open Matrix →Create free account

Who Matrix is for

For decisions with several plausible options and no obvious winner, where the discussion keeps going in circles because nobody has said out loud what they are actually optimising for. Writing the weights down usually ends the argument.

What Matrix does

Matrix makes a trade-off explicit rather than intuitive. You define criteria, weight them, and score each option, and the result shows not just which option wins but which criterion carried it. Then you change a weight and see whether the answer survives — a conclusion that flips when one weight moves slightly is a conclusion you should not act on, and a matrix is the fastest way to discover that. AI suggests criteria you might have missed, which you accept or reject.

Weighted criteria scoring across options
Shows which criterion drove the result
Sensitivity testing by changing weights
Exports the reasoning, not just the winner

How Matrix works

List the options
Everything genuinely on the table, including the one nobody likes.
Define and weight criteria
The weights are the decision. Setting them honestly is uncomfortable and it is where the value is.
Score each option
Per criterion. Scoring forces you to defend intuitions that had not been examined.
Test the sensitivity
Move a weight and watch. An answer that flips easily is not an answer yet.

What Matrix does not do

Matrix computes from what you enter, so it inherits your scores and your weights entirely — it cannot tell you that a criterion is missing or that a score is wishful. It is not a decision-maker: a number produced by an honest process is still an input to judgement. And a matrix is a poor fit for decisions dominated by a single hard constraint, where the arithmetic dresses up a foregone conclusion.

Common questions

Does it decide for me?

No. It makes the trade-off explicit and shows what drove the result. If the answer surprises you, that is usually informative about your weights rather than about the options.

Why does sensitivity testing matter?

Because an answer that flips when one weight moves a little is not robust enough to act on. Discovering that before the decision is much cheaper than discovering it afterwards.

What if I score things optimistically?

Then the result is optimistic. Matrix computes from what you enter and cannot detect wishful scoring — which is why exporting the reasoning, not just the winner, is the point.