Classify cold email replies for AI inbox triage
A production-grade classifier for the 'Categorize Reply' node in an n8n, Make, or custom pipeline. Every inbound reply becomes structured JSON — category, objection type, extracted referral contacts, OOO return dates, and a requires_human flag with explicit escalation rules — so your automation routes interested replies to reps in minutes while objections and OOOs feed the right follow-up branches.
You are a reply classification engine for a cold email inbox. You receive the raw text of one inbound reply to an outbound campaign. You output ONLY a JSON object — no preamble, no explanation, no markdown fences — matching this exact schema:
{
"category": "interested" | "objection" | "referral" | "ooo" | "negative",
"confidence": 0.0-1.0,
"objection_type": "budget" | "timing" | "incumbent" | "no_need" | "trust" | "other" | null,
"referred_contact": { "name": string or null, "email": string or null, "title": string or null } | null,
"return_date": "YYYY-MM-DD" or null,
"requires_human": boolean,
"summary": one sentence, max 20 words
}
Classification rules:
- "interested": any positive engagement — questions about the product, pricing curiosity, meeting acceptance, 'tell me more'. Pricing questions are interested, not objection.
- "objection": pushback that leaves the door open — budget, timing, incumbent vendor, 'we tried this before'. Set objection_type.
- "referral": redirects to another person. Extract referred_contact fields; never invent values not present in the text — use null.
- "ooo": autoresponders. Extract return_date if stated; if a delegate is named, ALSO populate referred_contact.
- "negative": clear rejection, unsubscribe demands, hostility, legal threats.
- requires_human is true for: legal/compliance language (GDPR, CCPA, lawsuit, attorney), threats, press or partnership inquiries, existing-customer confusion, anything sexual or abusive, and any reply where confidence is below 0.7.
- Ambiguous polite brush-offs ('thanks, we're all set') are "objection" with objection_type "no_need", not "negative".
- If the reply quotes the original outbound email plus a one-line answer, classify based on the one-line answer only.
- Non-English replies: classify normally, note the language in summary.
- If the text is empty or pure signature/legal boilerplate, output category "negative", confidence 0.3, requires_human true.
Output the JSON object and nothing else.How to use it
- 1
Paste the prompt as the system prompt in your pipeline's LLM node (n8n's Claude or OpenAI node, or a direct API call), with the reply text as the user message.
- 2
Wire the JSON output to a router: interested pings the rep on Slack instantly, referral and ooo feed contact-extraction branches, requires_human goes to a review queue.
- 3
Test against 20 real replies from your inbox before going live — check the ambiguous ones (polite brush-offs, quoted-thread replies) land where you expect.
- 4
Log every classification next to the eventual human judgment for a week, then tighten the rules section with any recurring misses.
Best practices
Keep the requires_human threshold conservative at first — a false escalation costs a minute of review; a hostile reply answered by automation can cost the domain.
Strip email signatures and quoted threads before classification where you can — n8n's code node can cut everything after the first quoted line — accuracy jumps noticeably.
Route by confidence, not just category: an 'interested' at 0.95 can trigger an instant rep alert, while 0.72 goes to the review queue first.
Re-run your misclassification log monthly and patch the rules — every inbox develops its own weird reply patterns.
Example: what this looks like in practice
A RevOps lead at a company sending 15,000 cold emails monthly wires this into n8n between the Smartlead reply webhook and a router node. Across the first week it processes 340 replies: 41 interested (reps alerted on Slack within two minutes, versus the next-morning average before), 63 objections tagged by type and fed into tailored follow-up branches, 12 referrals with contacts extracted straight into a Clay table, 96 OOOs scheduling their own re-sends, and 128 negatives auto-suppressed. Nine replies flag requires_human — including one GDPR demand and one confused existing customer that automation would have mangled. Reply-to-meeting conversion rises simply because interested replies stopped waiting overnight.
Best fit
This prompt is one gear in a bigger machine. We orchestrate 35+ tools into outbound systems our clients own , and re-earn the results every month.
Book your free ICP review → →Frequently asked questions
Can AI accurately classify cold email replies?
Yes — modern LLMs handle the five core categories (interested, objection, referral, OOO, negative) with high accuracy when the prompt defines edge cases explicitly: pricing questions count as interested, polite brush-offs are objections rather than negatives, quoted threads are classified by the new text only. The requires_human flag catches the genuinely ambiguous rest. Expect strong day-one performance and better-than-junior-SDR consistency after a week of rule tightening.
How do I connect a reply classifier to Smartlead or Instantly?
Both tools fire webhooks on reply events. Point the webhook at an n8n or Make workflow: the reply text goes into an LLM node running this prompt as the system message, and the returned JSON feeds a router — interested to Slack alerts, referrals to contact extraction, negatives to suppression, requires_human to a review queue. The whole pipeline is an afternoon's build for anyone comfortable in n8n.
Which replies should always escalate to a human?
Anything with legal or compliance language (GDPR, CCPA, attorney, lawsuit), threats or abuse, press and partnership inquiries, existing customers who received cold outreach by mistake, and any classification below your confidence threshold — 0.7 is a sensible start. These are low-volume but high-consequence: a mishandled legal demand or a customer emailed by your outbound automation causes damage far beyond one thread.
More reply & objection handling prompts
Convert a positive reply into a booked meeting before it cools off
Closes the gap where outbound pipelines leak most — between 'interested' and 'booked'. The reply answers their question in one line, offers two concrete times plus a link, and names the meeting payoff, all under 70 words. The pre-written 48-hour bump catches the prospects who meant yes but got busy, which is most of them.
Handle 'we already have a vendor' without trashing the competitor
Turns the most common outbound objection into a wedge conversation. Instead of attacking the incumbent or retreating, the reply legitimizes the prospect's choice and probes one specific gap as a question — the only competitive angle that doesn't insult their judgment. The interview forces you to name a provable differentiator, and the model pushes back if it's generic.
Answer 'can you guarantee results?' without overpromising or dodging
Drafts a direct, evidence-led reply to a guarantee question without inventing contractual terms or outcome promises. It turns the conversation toward scope, proof, ownership, reporting, and the actual engagement terms.