From forecasting the future to simulating it
Shadow starts by telling you what your users will do if you change nothing. The endgame is a living, owned model of your users' behavior you can query and stress-test — before you ship.
Shadow Preview: the do-nothing forecast
Read-only, segment-level behavioral trajectories from your own product data: the 7/14/30-day decline, the confidence band, the worst case, and the cost of inaction — backtested and calibrated. No PII, no writes.
- Segment-level forecasts
- Calibrated confidence
- Cost of inaction
What-If: counterfactual simulation
Move from "what happens if we do nothing" to "what happens if we ship this." Simulate a change — a gating tweak, a new flow, a pricing move — against your cohorts and see the projected impact, who's affected, and the second-order effects, before a single user is touched.
From cohorts to individuals
Shadow forecasts cohorts today — a rate, its trajectory, its uncertainty. The next step is finer grain: modelling how an individual user moves through your product, so a what-if can be run against the specific people it would land on rather than against an average. Two things make that possible. Learning per-user behaviour from the events you already send, and replaying a real flow in a real client with side effects mocked — no emails, no charges, no writes — so the model is checked against how your product actually behaves on the device your users hold.
Owned models of your users' behavior
A living replica of each of your users, learned from their real behaviour rather than assembled from personas or interviews. Ask it what a change would do before you ship it, stress a decision against the people it actually affects, and see the risks that only exist in the counterfactual — the version of next quarter you chose not to run. It is yours, it compounds as your users generate more behaviour, and it is the asset no analytics tool can hand you.
You never have to take the next step on faith. The forecast is something you can check against your own history in week one — so by the time we ask you to trust a simulation, you have already watched us be right, and seen where we were wrong.