AI Cold Email

Deliverability is infrastructure work before it's a copywriting problem. The build uses dedicated sending domains separate from your primary domain, typically two to four subdomains or secondary domains warmed over four to six weeks before any real prospect volume goes through them. Warmup runs through a pool of real inboxes exchanging genuine-looking traffic to build send history with the major inbox providers. Once live, daily send volume per domain stays well under the thresholds that trip spam filters, and the system rotates across domains so no single one carries the full load. DKIM, SPF, and DMARC records are set correctly on every sending domain before the first email goes out. That's non-negotiable, and it gets verified during setup rather than assumed. Bounce handling is automated: hard bounces remove the address immediately, soft-bounce patterns flag the account for review instead of retrying forever. Reply rates and spam complaint rates get monitored weekly, and if a domain's reputation slips, it gets rotated out and replaced before the damage spreads.

CAN-SPAM applies to commercial email sent to US recipients and has specific requirements: a physical mailing address in every email, a clear unsubscribe mechanism that processes opt-outs within ten business days, no deceptive subject lines, and accurate sender identification. The build includes all of these by default. Unsubscribe links sit in every email, opt-outs suppress the prospect from future sends immediately, and the sending domain traces back to a real business entity. CASL covers Canadian recipients and is stricter, requiring express or implied consent before sending, so Canadian prospects get flagged for a consent-qualified workflow rather than cold outbound. GDPR covers EU-based recipients, and B2B cold email to EU prospects operates under a legitimate interest basis that requires a documented balancing test. If EU accounts are in scope, that documentation is part of the engagement deliverables. The build does not scrape email addresses from websites in ways that violate terms of service. List sources are Apollo, Clay, ZoomInfo, or similar commercially licensed databases where the provider has handled consent obligations on their end.

Lists come from two places: accounts your team already has in CRM that haven't been worked, and new account discovery through Apollo or Clay using your ICP criteria (industry, headcount, revenue band, geography, tech stack). Enrichment accuracy varies by field. Company-level fields like industry, headcount, and funding stage are generally reliable from licensed providers. Direct email addresses tend to run sixty to eighty percent accurate depending on the source and how recently the contact changed roles, and bounce handling cleans the rest automatically. The personalization signals (funding events, job postings, news mentions) are pulled from live sources at send time rather than a static export, so they reflect what's actually happening at the account now, not three months ago when someone pulled a CSV. AI-generated first lines are written strictly from the sourced data, never invented. If the enrichment layer can't find a specific signal for an account, the system falls back to a verified general hook instead of fabricating a detail.

Honest answer: it depends on your ICP, your offer, and how competitive the channel is in your target market. Reply rates for well-built, signal-triggered B2B sequences tend to run higher than blast campaigns, but quoting a specific percentage before seeing your list, your offer, and your competitive landscape would be selling, not engineering. What the build controls is the floor. Correct deliverability setup keeps emails reaching inboxes instead of spam folders, which is the first gate. Relevant personalization lifts open-to-reply conversion over generic templates. Signal-triggered timing, reaching a prospect right after a relevant event, improves response probability because the outreach is contextually relevant rather than random. Ongoing support includes A/B testing on subject lines, first lines, and call-to-action framing, so response rates get tracked weekly and the sequences improve over time on actual data from your list. If response rates fall short after sixty days, we audit list quality, offer clarity, and signal sources before concluding the channel doesn't fit the use case.

Reply classification runs automatically. Positive replies (interest, questions, requests for more information, meeting requests) pause the prospect's sequence immediately so no follow-up fires into an active conversation. The reply gets restricted, the thread context gets logged to CRM with a note summarizing the prospect's enrichment data and sequence history, and an alert goes to the assigned rep with the full thread and a suggested next step. Neutral replies ("not right now," "maybe next quarter," "forward to X") get handled by rules set during the build: some go into a nurture sequence, some get logged for a defined follow-up date, some route to a different contact at the account. Negative replies and unsubscribes suppress the prospect from all future sends permanently. The rep's job starts at the positive reply; everything before that is the system's job. The handoff is designed so the rep has everything they need in the first notification: who the prospect is, what signal triggered the outreach, what was sent, and what the prospect said. No digging through a tool to reconstruct context before responding.