JEV maxxing for marketers

Dmitry Korzhov laid out seven places JEV replaces hours of manual marketing analysis, reporting it speeds up most marketing workflows 30x for under $3.

Original post "Jevmaxxing for marketers. Jev can speed up most marketing workflows 30x and do it for < $3" Read the original post on X

The seven use cases

1. Scan the whole Meta Ad Library. It reads every live ad in your category and tags each one by hook, format, offer, and days running.
2. Find the ad patterns that survive. It compares formats by how many ads are still live after 60 days, so you know what lasts before you test it.
3. Score briefs before you shoot. Your LLM writes the briefs, JEV scores each on hook, brand fit, and survival odds, and only the top ones get made.
4. Sort search terms. It asks "is this query from a buyer?" across the full Google Ads report, so negatives land the same night.
5. Catch fatigue early. For every ad with frequency up and CTR down, it picks replace, refresh, or leave.
6. Check ad to landing page match. It scores whether the page delivers what the ad promised, the cheapest CVR fix in most accounts.
7. Score every lead. It rates each form fill 0 to 100 against your ideal customer within seconds, so Google and Meta learn to find more of the good ones.

Why JEV is the right model here

Every one of those steps has the same shape: many inputs, one standard, a judgment per input. That is a decision problem, not a writing problem. An LLM would describe each ad in paragraphs and bill you for the tokens. JEV returns the tag or a score per input, in parallel.

Available in the Ryze AI app and as an MCP / Claude connector.

How to copy it

1. Pick one repetitive judgment you already make by hand.
2. Write the standard down as a fixed set of options or a 0 to 100 scale.
3. Feed the raw inputs in, take the scores out, and only spend time on what clears the bar.

Related

• JEV overview