Predicting how customers, hires, launches, and partners actually behave.
Most business decisions are bets dressed up as plans. You set a price, ship a product, make a hire, sign a partner — and you find out months later whether the bet paid off. Prediction closes that gap. Instead of waiting for the market to grade your decision, you describe the move to MiroFish and get back a structured view of how it's likely to land: the optimistic path, the base case, and the version where it goes sideways, each with a probability and the trigger that flips one into another.
What makes business scenarios well suited to AI prediction is that they usually have observable inputs. A price change has a current ARPU, a customer count, a rough sense of demand elasticity. A launch has a market, a comparable, a budget. The predictor grounds its branches in whatever you give it and is explicit about what it had to assume — so when you disagree with an assumption, you can change it and watch the prediction update rather than arguing with a black box.
This cluster covers the business predictions operators ask for most: how customers react to a price change, whether a launch succeeds, how a hiring decision plays out, and how a cofounder split is likely to unfold. Each post is built around a real decision and shows how to turn it into a question the AI can predict well — specific, quantified where possible, honest about the unknowns.
The goal isn't to outsource the judgment. It's to pressure-test it. A prediction you can argue with is worth more than a forecast you have to take on faith, because the argument is where you discover the assumption you didn't know you were making. Read the post nearest your decision, then predict your own version and see which branch the numbers actually favor.
Describe your scenario and MiroFish predicts the likely outcomes — with probabilities and the reasoning behind each one.
Cofounder conflict rarely ends in a clean break — it ends in slow drift. Here's how to predict how the conflict actually plays out, and the forcing function that decides which way it goes.
A hire is a high-variance bet you can't easily reverse. Here's how to predict how a hiring decision plays out — and why the manager, not the candidate, is usually the deciding factor.
Most launches are graded months too late to do anything about it. Here's how to predict a launch's first 90 days up front — the branches, the real success driver, and the early tripwire.
A price change is one of the most predictable business moves — if you frame it right. Here's how to predict churn-adjusted impact, and why usage, not price, is the variable that decides it.