Emails that read like a human did the research.
Our writing model wasn't trained on the open internet. It was trained on 25 million real B2B outbound emails, the ones that worked, and the ones that didn't. The output isn't merge fields with a name.
Live, the writer types the opening line.
Most "AI email writers" are GPT-4o with a prompt that says "write a cold email." The output is technically grammatical, but it has the unmistakable flavor of a model that learned email from the open internet. We built ours differently.
- The model reads each prospect. Funding, hiring, product launch, award, partnership, whatever signal is freshest. It writes to that one.
- 6× reply rate vs templates. Same data inputs, different outcomes.
- Skips weak personalization if the signal isn't there. No filler.
Hi Andrea,
Best,
Maya
Research, write, pick a winner. Three steps, every send.
When ReachIQ writes an email for a specific prospect, three things happen at once.
Crawlers research
Recent posts, company news, funding, hiring, awards, events, partnerships, tech stack, GitHub commits, Crunchbase.
Model writes
Research feeds the writing model along with vertical, role, and prospect signal. No fixed variants. The model writes to whatever matters most for this prospect.
Best variant picked
The model recommends the strongest opener based on signals it surfaced. Your SDR can override.
Sent on cadence
Email goes out at the prospect's optimal send time, tracked per recipient. Auto-pause on reply.
What "25 million emails" shows.
Other tools claim "AI personalization" and mean merge fields with a name. We mean a writing model that has seen what works in your specific industry, at your specific stage, for your specific buyer.
A fintech VP Sales doesn't open like a healthcare CIO.
Different opening lines, different proof points, different objections. The model knows the patterns from millions of sends in each segment.
A Series A founder doesn't respond like a Fortune 500 buyer.
Stage-of-company signals tune the writing voice. Procurement language to enterprise. Founder language to startup.
A 50-person CTO reads emails differently than a Stripe CTO.
Company size is its own model dimension. Our training set covers the entire spread, so the output knows the difference.
Proof, not pitch. Same prospect, two emails.
Templated personalization is merge fields with a name. We mean something different.
I noticed you work at {{company}} as a {{title}}. Curious, are you looking to scale outbound at {{company}}?
We help {{industry}} teams book more meetings. Open to a quick call?
Congrats on the Series B last week. Saw you're scaling the sales team and hiring 4 SDRs this quarter. Curious if you're building outbound in-house or evaluating partners while the team ramps.
We helped Linear hit their first 50 enterprise meetings post-Series-A in 6 weeks. Happy to share, no pitch.
What we don't do, and won't.
The model is fine-tuned to do the opposite of generic, and to refuse when the data isn't there for genuine personalization.
What other AI writers still do.
If your "AI" still generates these, it's just GPT-4o with a thin prompt.
- 47-word "Hope you're well!"Filler opener that asks for nothing. Skip rate, ~85%.
- "Your impressive work in [vague space]"The classic LLM hedge. Reads as confessional fake-flattery.
- "I came across your profile"Universally read as a sign the writer didn't look.
- "I'd love to learn more about your role"The "tell me about you" trap. SDRs send this when they have nothing to say.
- Faking a personal connectionPretending to have met at a conference, mutual friend, etc.
Real signal, or no email at all.
If the writer can't find a real signal worth referencing, it skips the prospect rather than fall back to a template.
- References the actual signalRecent funding, role change, product launch, hire, news mention.
- Reads the freshest signalFunding, hiring, product launch, award, partnership. The model picks per prospect.
- Specific proof in the second paragraphA named customer in the same stage / industry / role.
- One clear askEither a 15-minute call or a single resource. Never both.
- No-fit skipIf no signal worth quoting exists for the prospect, the AI doesn't send.
Hyper-personalization, explained.
The rest of the platform.
See the writer write live.
20-minute live demo. Bring a real prospect, watch the AI draft, ship the strongest variant.