AI personalizes onboarding sequences based on user role, use case, and engagement signals. Higher activation rates with less manual effort.
Generic onboarding emails don't address individual user needs. A developer building an AI agent has different priorities than a marketing manager exploring email campaigns. One-size-fits-all drip sequences lead to low activation rates, early churn, and wasted potential in users who never discover the features most relevant to them.
MultiMail's AI agent sends personalized onboarding sequences that adapt to each user's behavior. It monitors which features users have explored, adjusts email content to highlight the most relevant next steps, and changes timing based on engagement signals. Monitored oversight keeps your team informed while the agent drives activation autonomously.
Your application notifies the AI agent when a new user signs up, including profile data like role, use case, and company size for initial segmentation.
The agent classifies the user into a segment (developer, marketer, ops manager, etc.) to determine which onboarding track to follow.
The agent sends onboarding emails at intervals determined by user engagement. If a user completes a step quickly, the next email arrives sooner. If they stall, a nudge email is sent with additional help resources.
Under monitored oversight, your team can review the onboarding log to see which sequences drive the highest activation rates and refine the content accordingly.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def start_onboarding(user: dict):
segment = classify_user_segment(user)
first_step = get_onboarding_step(segment, step=1)
response = requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": user["email"],
"subject": first_step["subject"],
"text_body": personalize_content(
first_step["body"], user["name"], segment
),
"html_body": first_step["html"]
}
)
"cm"># Track in contacts
requests.post(
f"{API}/contacts",
headers=HEADERS,
json={
"email": user["email"],
"name": user["name"],
"metadata": {
"segment": segment,
"onboarding_step": 1,
"signup_date": user["created_at"]
}
}
)Classify a new user and send the first onboarding email.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def check_and_advance_onboarding(user: dict):
"cm"># Get current onboarding state
contact = requests.get(
f"{API}/contacts/search",
headers=HEADERS,
params={"email": user["email"]}
).json()["contacts"][0]
current_step = contact["metadata"]["onboarding_step"]
segment = contact["metadata"]["segment"]
"cm"># Check if user completed the current step's action
completed = check_user_progress(user["id"], current_step)
if completed:
next_step = get_onboarding_step(segment, current_step + 1)
if next_step:
requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": user["email"],
"subject": next_step["subject"],
"text_body": next_step["body"]
}
)Check user progress and send the next relevant onboarding email.
"cm">// Using MultiMail MCP tools for onboarding sequences
async function startOnboarding(user: NewUser) {
const segment = classifySegment(user);
const step = getOnboardingStep(segment, 1);
"cm">// Send welcome email
await mcp.send_email({
to: user.email,
subject: step.subject,
text_body: personalizeContent(step.body, user.name, segment)
});
"cm">// Track onboarding state
await mcp.add_contact({
email: user.email,
name: user.name,
metadata: {
segment,
onboarding_step: 1,
signup_date: new Date().toISOString()
}
});
}
"cm">// Check progress and advance
async function advanceOnboarding(user: User) {
const contacts = await mcp.search_contacts({ query: user.email });
const meta = contacts[0].metadata;
if (userCompletedStep(user.id, meta.onboarding_step)) {
const next = getOnboardingStep(meta.segment, meta.onboarding_step + 1);
if (next) {
await mcp.send_email({
to: user.email,
subject: next.subject,
text_body: next.body
});
}
}
}Manage onboarding sequences using MultiMail MCP tools.
Personalized onboarding emails that address each user's specific use case drive significantly higher activation rates than generic drip sequences.
The AI sends emails when they're relevant — immediately after a user completes a step or after a configurable delay if they stall. No more rigid day-1, day-3, day-7 schedules that ignore actual user behavior.
Developers get API docs and code examples. Marketers get campaign setup guides. Each user segment receives content relevant to their role and use case.
Monitored oversight lets your team review which onboarding paths drive the best activation. Iterate on content and timing based on real data.
Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a 38-tool MCP server. Formally verified in Lean 4.