Structural diagnostic for startups – reading the system, not the surface.
Most startup decisions fail before the data is even read. Founders treat metrics as causes, when metrics are almost always effects of something deeper.
Two startups with identical revenue, burn, and growth curves can sit in completely different structural positions – one stable, one collapsing – and standard analysis cannot tell them apart.
Most advice operates at the surface. So do most tools. So do most pitch reviews. The result is predictable: founders fix the wrong thing, investors back the wrong pattern, and outcomes look random when they are not.
StartupDiag evaluates how a startup's operating dimensions interact under load, identifies the binding constraint shaping its trajectory, and traces the mechanism producing the observed behavior.
The output is specific. One constraint, named. One mechanism, explained. One action sequence, ordered.
Built on a structural framework developed through years of systems design practice.
StartupDiag runs a deterministic structural analysis. It is designed to produce consistent structural readings from the same input conditions. There is no opinion layer, no model preference, no soft framing – only the reading the data supports.
Where AI tools generate text, StartupDiag returns a system-level reading shaped strictly by what the data contains.
They exist to remove ambiguity at the moments where ambiguity is most expensive.
For founders making a priority decision: which constraint to remove first, what to fix in what order, what the system will likely do under each path.
For investors and decision-makers under capital pressure: where the structural risk sits, what the data supports as a position, what to trust, and what to question.
In both cases, the report is a decision instrument – not a description of the past.