Industry-Specific AI Solutions

// n8n workflow: WooCommerce Order → Keap CRM → Stripe
// 1. WooCommerce order webhook
const order = {
  id: 12345,
  customer_email: "[email protected]",
  total: 299.99,
  items: [...],
  status: "completed"
};

// 2. Create/Update contact in Keap CRM
const contact = await keap.createOrUpdateContact({
  email: order.customer_email,
  firstName: order.billing.first_name,
  lastName: order.billing.last_name,
  tags: ["customer", "purchased"],
  customFields: {
    lifetime_value: calculateLTV(order.customer_email),
    last_purchase_date: new Date(),
    last_purchase_amount: order.total
  }
});

// 3. Process payment via Stripe
const payment = await stripe.paymentIntents.create({
  amount: order.total * 100,
  currency: "usd",
  customer: contact.stripe_customer_id,
  metadata: {
    order_id: order.id,
    keap_contact_id: contact.id
  }
});

// 4. Trigger automation sequence
if (order.total > 200) {
  await keap.addToSequence(contact.id, "vip-customer-nurture");
} else {
  await keap.addToSequence(contact.id, "post-purchase-upsell");
}

// 5. Sync to Google Sheets for reporting
await sheets.appendRow({
  orderId: order.id,
  customerEmail: order.customer_email,
  revenue: order.total,
  crmContactId: contact.id,
  stripePaymentId: payment.id,
  date: new Date()
});

// Insurance Verification
async function verifyInsurance(patientData) {
  const result = await fetch('https://api.eligibility-check.com/v2/verify', {
    method: 'POST',
    body: JSON.stringify({
      memberId: patientData.insurance_id,
      provider: patientData.insurance_provider,
      serviceDate: appointmentDate
    })
  });
  
  return {
    isEligible: result.eligible,
    copay: result.copay_amount,
    deductible: result.deductible_remaining
  };
}

# Python - Real-time signal processor
import ta  # Technical Analysis library

def calculate_signals(symbol, timeframe='1h'):
    df = get_market_data(symbol, timeframe, limit=100)
    
    # Calculate indicators
    df['rsi'] = ta.momentum.RSIIndicator(df['close'], window=14).rsi()
    df['macd'] = ta.trend.MACD(df['close']).macd()
    df['bb_high'] = ta.volatility.BollingerBands(df['close']).bollinger_hband()
    
    latest = df.iloc[-1]
    
    # Oversold + Volume spike
    if latest['rsi'] < 30 and latest['volume'] > latest['volume_sma'] * 1.5:
        return {
            'type': 'BUY',
            'strength': 'STRONG',
            'reason': 'RSI oversold with volume confirmation'
        }

# Python - Automated essay evaluation
@celery.task
async def grade_essay(essay_id, essay_text, assignment_context):
    # Plagiarism check
    plagiarism_score = await check_plagiarism(essay_text)
    
    # Multi-model evaluation
    claude_eval = await anthropic.messages.create(
        model="claude-3-opus-20240229",
        messages=[{"role": "user", "content": prompt}]
    )
    
    gpt_eval = await openai.ChatCompletion.acreate(
        model="gpt-5.2",
        messages=[{"role": "user", "content": prompt}]
    )
    
    # Aggregate scores
    final_score = (claude_scores['total'] + gpt_scores['total']) / 2

# Python - ML-based failure prediction
import tensorflow as tf
from sklearn.preprocessing import StandardScaler

def predict_failure(sensor_data):
    # Features: temperature, vibration, pressure, runtime
    features = StandardScaler().fit_transform(sensor_data)
    
    model = tf.keras.models.load_model('failure_predictor.h5')
    prediction = model.predict(features)
    
    failure_probability = prediction[0][0]
    
    if failure_probability > 0.75:
        # Alert maintenance team
        send_alert({
            'urgency': 'HIGH',
            'machine_id': sensor_data.machine_id,
            'predicted_failure': 'within 24 hours'
        })
    
    return failure_probability

// AI Lead Scoring
const scoreLead = (lead) => {
  let score = 0;
  
  // Budget alignment
  if (lead.budget >= property.price * 0.9) score += 30;
  
  // Timeline urgency
  if (lead.timeline === 'immediate') score += 25;
  
  // Pre-approval status
  if (lead.preApproved) score += 20;
  
  // Engagement level
  score += (lead.pageViews * 2);
  score += (lead.emailOpens * 3);
  
  if (score > 70) {
    scheduleCallWithAgent(lead);
  } else if (score > 40) {
    sendVirtualTourLink(lead);
  } else {
    addToNurtureCampaign(lead);
  }
}

// n8n Telegram/WhatsApp Bot Handler
// Webhook receives message
const message = {
  from: customer_phone,
  text: "Do you have the blue dress in size M?",
  platform: "whatsapp"
};

// Check inventory with AI
const aiResponse = await queryProductDatabase({
  query: message.text,
  context: "inventory_catalog"
});

// Send response
if (aiResponse.in_stock) {
  await sendMessage({
    to: message.from,
    text: `Yes! We have the blue dress in size M for $89. Would you like to reserve it? Reply YES to hold it for 24 hours.`,
    platform: message.platform
  });
} else {
  await sendMessage({
    to: message.from,
    text: `Sorry, that item is currently out of stock. Would you like me to notify you when it's back? We also have similar items in stock.`,
    platform: message.platform
  });
}