How to Use AI for Cold Email (112 Opportunities in 7 Days)

How do you use AI for cold email?

Most AI cold email is worse than no cold email, because people point a language model at a name and a company and ask it to be charming. The result reads exactly like what it is.

The version that works inverts the order: get real data first, then write. Alec Saluga demoed this live on Session 006 and it is deliberately simple. In his words, this is not 25 agents, this is more like one agent, and it goes to show you do not need something incredibly complex to get real results.

What results should you actually expect?

These are the numbers from a screenshot Alec pulled right before the session, covering the last seven days with one client.

The context matters more than the headline. Alec's own baseline: a 3% reply rate is typically considered pretty good, and half of those replies might be telling you to stop emailing them. That is what a normal cold-email operation looks like, and it is why the opportunity rate here is worth examining rather than just admiring.

One honest caveat: the client is not named, and the 3,500 figure is Alec's estimate on tape ("it's probably 3,500 emails-ish"), not a reported number.

Where does the prospect data come from?

Data is the real bottleneck, not the writing. Apify is the answer Alec points people to: a store of off-the-shelf scrapers you can run without building anything.

You can scrape Facebook pages for emails, TikTok accounts for analytics, Google search results, or, in Alec's case, a lawyer and attorney lead scraper, because one of his businesses sells to attorneys.

The agent handoff is the part worth copying: you give the agent the scraper ID and an API key, and it runs the scraper itself and pulls the data in. The agent is not just writing, it is fetching.

How does the AI personalize each email?

The scraped dataset is the starting point, not the personalization. The enrichment step is where the agent goes to the prospect's own website and pulls out something real: how long they have been around, a case they just settled.

Then the email references it. "I see you've been around since 1997." "I saw you just settled this case for this amount, we can get you five more of those."

It is the same instinct as the blue-bubble text message from earlier in the session: the extra layer of effort is what signals a human did the work, and that is what moves reply rates.

“it can go on the website and pull out some stats from the law firms, like, hey, I see you've been around since 1997”

Alec Saluga · 52:39

How do you send at scale without burning your inbox?

The finished emails push into Instantly, which is the cold-email software behind the screenshot. Its job is deliverability: sending at volume without torching your personal domain and inbox reputation.

That is the whole loop. Pull in data, write personalized emails tailored to both the prospect and your offer, push into a sequence, get meetings on autopilot. It just runs.

What is not built yet, and Alec is explicit about this: the agent that replies to the responses and sets the meetings. That is the next layer, along with pushing it all into a CRM. Worth knowing before you assume the loop closes itself.

What does this look like at full scale?

Seven months of running this system produced numbers worth updating this post for. Alec Saluga's Instantly account sent 110,000 personalized emails between January 1 and August 2, 2026, at a 3% reply rate, generating almost 1,000 opportunities. His time in the system: maybe a couple hours a week, probably an hour, fully automated.

The pipeline behind those numbers is the same one this post teaches: a data source (Apify scraping or an Apollo contact database) feeds an LLM prompted to write each personalized email, which feeds Instantly for distribution. Volume is an infrastructure game, because each inbox is constrained to about 30 emails a day to keep deliverability high; growth means adding inboxes and domains, not pushing one inbox harder.