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← Monday's Prompts

Automate Sales Enablement 🚀

Turn Monday's 3 prompts into production-ready code

June 10, 2025
30 min read
💼 Sales🐍 Python + TypeScript⚡ 0 → 1000 leads/day
On This Page:ProblemDemoCodeScaleGotchasCost

The Problem

On Monday you tested the 3 prompts in ChatGPT. You saw how prospect research → email drafting → follow-up scheduling works. But here's the reality: your sales team can't manually research 100 prospects per day. One SDR spending 3 hours on manual research and email crafting? That's $75/day in labor costs. Multiply that across a 10-person sales team and you're burning $195,000/year on tasks AI could handle in minutes. Plus the inconsistency that leads to missed opportunities.

3+ hours
Per day on manual research and emails
60% waste
From poor targeting and generic outreach
Can't scale
Beyond 10-15 quality leads/day per rep

See It Work

Watch the 3 prompts chain together automatically. This is what you'll build.

Watch It Work

See the AI automation in action

Live Demo • No Setup Required

The Code

Three levels: start simple, add reliability, then scale to production. Pick where you are.

Basic = Quick startProduction = Full featuresAdvanced = Custom + Scale

Simple API Calls

Good for: 0-100 leads/day | Setup time: 30 minutes

Simple API Calls
Good for: 0-100 leads/day | Setup time: 30 minutes
# Simple API Calls (0-100 leads/day)
import openai
import json
import os
from typing import Dict, List
from datetime import datetime, timedelta

# Set your API keys
openai.api_key = os.getenv('OPENAI_API_KEY')

def automate_sales_enablement(prospect_data: Dict) -> Dict:
    """Chain the 3 prompts: research → email → schedule"""
    
    # Step 1: Research and enrich prospect
    research_prompt = f"""Analyze this prospect data and identify:
Showing 15 of 111 lines

When to Level Up

1

Simple API Calls

  • Sequential processing
  • Simple error handling
  • Manual CRM updates
  • Basic email scheduling
  • Local logging
Level Up
2

Add Reliability Layer

  • Retry with exponential backoff
  • CRM API integration (Salesforce/HubSpot)
  • Email provider integration
  • Structured logging
  • Basic metrics tracking
Level Up
3

Production Pipeline

  • SQS/Redis queue management
  • Concurrent processing (10-50 parallel)
  • Prometheus metrics
  • Result caching
  • Automated CRM webhooks
  • Analytics dashboard
Level Up
4

Enterprise System

  • Multi-agent orchestration
  • Load balancing across regions
  • Advanced analytics and A/B testing
  • Custom model fine-tuning
  • Dedicated infrastructure
  • 24/7 monitoring and alerts

Sales-Specific Gotchas

The edge cases that break sales automation in production

Email Deliverability & Spam Filters

Implement proper email authentication and warm-up sequences

Solution
# Email Deliverability Best Practices
import dns.resolver
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import dkim

class EmailDeliverability:
    def __init__(self, domain: str, dkim_private_key: str):
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CRM Data Sync & Deduplication

Implement robust deduplication and conflict resolution

Solution
# CRM Deduplication Strategy
from typing import Optional, Dict, List
import hashlib

class CRMDeduplication:
    def __init__(self, salesforce_client):
        self.sf = salesforce_client
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Rate Limiting & API Quotas

Implement intelligent rate limiting and quota management

Solution
# Intelligent Rate Limiting
import asyncio
from collections import deque
from datetime import datetime, timedelta
from typing import Callable, Any

class RateLimiter:
    def __init__(self, requests_per_minute: int, requests_per_day: int):
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Personalization at Scale

Build a multi-source data enrichment pipeline

Solution
# Multi-Source Data Enrichment
import aiohttp
import asyncio
from typing import Dict, Optional

class DataEnrichment:
    def __init__(self, clearbit_key: str, zoominfo_key: str, linkedin_key: str):
        self.clearbit_key = clearbit_key
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Compliance & Opt-Out Management

Build a comprehensive compliance and opt-out system

Solution
# Compliance & Opt-Out Management
from enum import Enum
from datetime import datetime, timedelta
from typing import Optional, List
import hashlib

class ConsentType(Enum):
    EXPLICIT = 'explicit'  # User explicitly opted in
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Adjust Your Numbers

500
105,000
5 min
1 min60 min
$50/hr
$15/hr$200/hr

❌ Manual Process

Time per item:5 min
Cost per item:$4.17
Daily volume:500 items
Daily:$2,083
Monthly:$45,833
Yearly:$550,000

✅ AI-Automated

Time per item:~2 sec
API cost:$0.02
Review (10%):$0.42
Daily:$218
Monthly:$4,803
Yearly:$57,640

You Save

0/day
90% cost reduction
Monthly Savings
$41,030
Yearly Savings
$492,360
💡 ROI payback: Typically 1-2 months for basic implementation
💼

Want This Running in Your Sales Team?

We build custom sales AI systems that scale from 10 to 10,000 leads/day. Production-ready code, CRM integration, and compliance built-in. Book a free 30-minute call to see how it works for your team.

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2026 Randeep Bhatia. All Rights Reserved.

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