AI detects user milestones and sends personalized feedback requests automatically. Behavioral triggers replace random timing.
Product teams lack qualitative feedback because they ask at the wrong time — either too early when users haven't formed opinions, or too late when they've lost interest. Generic feedback emails yield single-digit response rates, and the responses they do get are biased toward the most vocal users.
MultiMail's AI agent monitors user behavior for key milestones — first successful API call, 30-day mark, feature adoption — and sends personalized feedback requests at the optimal moment. Autonomous mode handles delivery instantly when milestones trigger, since templated feedback requests are low-risk.
Configure behavioral triggers that signal the right moment to ask for feedback: first API call, feature adoption, usage thresholds, or time-based milestones.
When a user hits a milestone, the AI agent composes a feedback request referencing their specific experience. A user who just tried email threading gets asked about threading, not generic product feedback.
With autonomous oversight, feedback requests are sent immediately when milestones trigger. No human bottleneck delays the ask past the optimal moment.
The agent uses check_inbox to monitor for replies, categorizes feedback themes, and surfaces actionable insights to your product team.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def send_feedback_request(user_email: str, user_name: str, feature: str):
response = requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": [user_email],
"subject": f"Quick question about your experience with {feature}",
"text_body": (
f"Hi {user_name},\n\n"
f"You&"cm">#039;ve been using our {feature} feature for 2 weeks now. "
f"We&"cm">#039;d love to hear: what's working well and what could "
f"be better?\n\n"
f"Just reply to this email with your thoughts — even a "
f"one-sentence answer helps us improve.\n\n"
f"Thanks,\nThe Product Team"
),
"html_body": f"<p>Hi {user_name},</p><p>You&"cm">#039;ve been using our {feature}...</p>"
}
)
return response.json()
# Triggered when user hits 2-week milestone
send_feedback_request("[email protected]", "Dev", "email threading")Trigger a personalized feedback email when a user hits a usage milestone.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
"cm"># Check for feedback replies
inbox = requests.get(
f"{API}/mailboxes/product-mailbox-id/inbox",
headers=HEADERS,
params={"status": "unread", "limit": 50}
).json()
for msg in inbox["emails"]:
email = requests.get(
f"{API}/emails/{msg[&"cm">#039;id']}",
headers=HEADERS
).json()
# Tag feedback by feature area
requests.post(
f"{API}/emails/{msg[&"cm">#039;id']}/tags",
headers=HEADERS,
json={"tags": ["feedback", "email-threading"]}
)
print(f"Feedback from {email[&"cm">#039;from']}: {email['text_body'][:100]}")Check the inbox for feedback responses and categorize them.
"cm">// Send feedback request at milestone
const result = await mcp.send_email({
to: "[email protected]",
subject: "Quick question about email threading",
text_body: "Hi Dev, you've been using email threading for 2 weeks..."
});
// Tag for tracking
await mcp.tag_email({
email_id: result.id,
tags: ["feedback-request", "threading", "2-week-milestone"]
});
// Later: check for responses
const inbox = await mcp.check_inbox({
mailbox_id: "product-mailbox-id",
status: "unread"
});
"cm">// Read and categorize each response
for (const msg of inbox.emails) {
const email = await mcp.read_email({ email_id: msg.id });
console.log(`Feedback: ${email.text_body}`);
}Use MCP tools to send feedback requests and process responses.
Behavioral triggers ensure you ask when users have a fresh, relevant experience to share. Timing matters more than the ask itself.
Each feedback request references the specific feature the user just experienced, making the ask feel personal rather than generic.
Autonomous mode sends feedback requests the moment milestones trigger, capturing users at peak engagement before the experience fades.
The AI agent categorizes incoming feedback by theme, sentiment, and feature area, surfacing actionable insights without manual review of every reply.
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