01 The engine
We spent three years and four million emails finding out what actually gets answered. The model learned it. Now it writes with it, for one person at a time.
The corpus
Three years of our own mail. Not a public dataset, not scraped. What went in was every email we sent and what came back; what comes out is the four decisions the engine makes about every email it writes.
What it learned
Every send came back as data. What got opened, what got answered, what got deleted. The model turned that into rules it writes with.
Which openers get opened, by which persona, at which seniority.
How many words earn a reply before attention runs out.
Soft ask or hard ask, meeting or question, per persona.
Where the personalisation sits, where the point lands.
Day and hour, per mailbox, per timezone.
The gap between touches, and how much runway a variant gets.
It has never seen your market, so the angles still get tested rather than assumed. And it does not decide whether your offer is any good. Nothing does that but your offer.
Per lead
The engine finds far more than it uses. A detail that is true but irrelevant reads as surveillance, so it gets thrown away.
Written into the email
Thrown away
True, and useless. Most agencies never show you the second column.
How an email gets written
The same sequence runs for every single lead. It does not get skipped when the list gets big.
Try the rules yourself
The model’s mechanical rules run in the open: paste your cold email and see what it would keep and what it would cut.
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EMPRA