HeatCheck vs. MadKudu.
MadKudu is a capable predictive-fit platform. The difference is transparency: MadKudu’s model tells you who fits; HeatCheck shows you, signal by signal, who is engaging right now.
The core difference
MadKudu builds a predictive model of who looks like a good customer. That is valuable for fit. But a predictive score is hard for a rep to interrogate, and fit is not the same as timing. HeatCheck is behavioral and transparent: every point traces to a specific signal on the record, and the score is about intent happening now, not a likelihood.
What HeatCheck does differently
Transparent, not predictive
Every point is an itemized signal a rep can read, not a model output to trust on faith.
Timing, not just fit
It measures engagement happening now, the moment to act, not a static likelihood.
The LinkedIn signal
Per-post engagement resolved to your contacts is a first-class input.
A built-in action layer
Crossings become screened tasks; falling heat becomes CS plays.
At a glance
As of July 2026.
| HeatCheck | MadKudu | |
|---|---|---|
| Scoring approach | Transparent behavioral signals | Predictive fit model |
| Rep can audit every point | Yes | Model output |
| LinkedIn engagement signal | Yes | Not a core input |
| Threshold crossing tasks | Yes | Varies |
| Falling-heat CS plays | Yes | Not core |
The short version.
Is fit scoring bad?
No, fit is useful. HeatCheck adds the timing and transparency a predictive fit score does not give a rep.
Can I combine fit and heat?
Yes. Fit tells you who is worth talking to; heat tells you when, and HeatCheck can gate on fit properties.
Why does transparency matter?
Because reps ignore scores they cannot interrogate. An itemized trail is what earns the trust.
Know who’s hot right now.
Early access is open for a small design-partner group.