Customer Communication Automation: Email, SMS, and Policy Controls
Overview
Automated customer communications accelerate response but create regulatory and brand risk if unmanaged. This guide defines template libraries, approval tiers, and channel policies.
Quick definition
Communication automation applies channel policies (frequency caps, quiet hours, consent flags) before any send—implemented as a decision layer, not scattered in templates.
Definition
Policy-controlled automation separates message intent (appointment reminder, payment due) from content variants—each variant reviewed and versioned.
Why it matters
TCPA, CAN-SPAM, industry-specific rules, and brand trust require controls—not ad hoc sends from individual tools.
Core framework
Step-by-step model as TypeScript interfaces (machine-readable checkpoints).
Template registry
/**
* Template registry
* Name, audience, channel, legal footer, and expiration for periodic review.
*/
export interface CoreFrameworkStep1TemplateRegistry {
/** Order in the core framework (0-based) */
readonly stepIndex: 0;
/** Display title for this step */
readonly title: "Template registry";
/** Narrative checkpoints as published in the guide */
readonly narrative: readonly string[];
}
export const CoreFrameworkStep1TemplateRegistry_NARRATIVE: readonly string[] = [
"Name, audience, channel, legal footer, and expiration for periodic review."
] as const;Draft vs send
/**
* Draft vs send
* AI drafts within approved slots; sensitive categories require human send.
*/
export interface CoreFrameworkStep2DraftVsSend {
/** Order in the core framework (0-based) */
readonly stepIndex: 1;
/** Display title for this step */
readonly title: "Draft vs send";
/** Narrative checkpoints as published in the guide */
readonly narrative: readonly string[];
}
export const CoreFrameworkStep2DraftVsSend_NARRATIVE: readonly string[] = [
"AI drafts within approved slots; sensitive categories require human send."
] as const;Detailed breakdown
Logic sections encoded as Python functions with structured narrative payloads.
Suppression and preference centers
def logic_block_1_suppression_and_preference_centers(context: dict) -> dict:
"""Operational logic: Suppression and preference centers"""
# Narrative steps from the guide (logic section)
paragraphs = ["Centralize opt-outs; sync back to CRM immediately."]
return {
"heading": "Suppression and preference centers",
"paragraphs": paragraphs,
"context_keys": tuple(sorted(context.keys())),
}Technical patterns
Policy as data
- `{ max_per_day, quiet_hours_tz, channels_allowed, consent_required }` per segment.
- Pre-send hook queries policy store; blocks emit audit reason code.
Code examples
Quiet hours gate
Converts recipient TZ; defers job if inside window.
export function canSendNow(recipientTz, quiet) {
const local = new Date().toLocaleString('en-US', { timeZone: recipientTz });
const hour = new Date(local).getHours();
return hour < quiet.start || hour >= quiet.end;
}System architecture
[Campaign / workflow trigger]
→ [Recipient resolution + consent DB]
→ [Policy engine: caps + quiet hours]
→ [Provider adapter: Twilio / SendGrid]
→ [Delivery webhooks → status table]Real-world example
A financial services team gated promotional SMS behind compliance-approved templates while allowing AI to personalize within fixed bounds.
Common mistakes
- Multiple tools sending without unified preference data.
- Letting models invent claims outside approved language.
Related topics
PrimeAxiom implements messaging automation with CRM-linked approvals—book a policy and template workshop.