AI and ChatGPT Integration

Learn how to enhance your email workflows with artificial intelligence using EmailEngine's OpenAI/ChatGPT integration capabilities.

Overview

EmailEngine integrates with OpenAI's API to provide AI-powered email processing capabilities:

• Email Summarization: Generate concise summaries of incoming emails
• Sentiment Analysis: Detect positive, neutral, or negative sentiment
• Event Extraction: Identify events and dates mentioned in emails
• Action Items: Extract tasks and due dates
• Fraud Detection: Assess risk of scam or phishing emails
• Reply Detection: Identify if sender expects a response
• Conversational Search: Ask questions about your email history

OpenAI API Access

• GPT-3.5: Available to all OpenAI accounts
• GPT-4: Available to all OpenAI accounts
• Paid Account: Recommended for production (free accounts have strict rate limits)

Feature 1: Email Processing and Summarization

Enable AI Processing

1. Navigate to Configuration → AI Processing in EmailEngine
2. Enter your OpenAI API key
3. Check "Enable AI Email Processing"
4. Select model (GPT-3.5 or GPT-4)
5. Save configuration

Model Selection

Model	Speed	Cost	Context Window	Best For
GPT-3.5 Turbo	Fast	Low	4K tokens	Summaries, basic analysis
GPT-4	Slower	High	8K+ tokens	Complex analysis, longer emails

Recommendation:

• Use GPT-3.5 for basic summarization
• Use GPT-4 for advanced features or longer emails

How It Works

When AI processing is enabled, EmailEngine automatically processes every new email arriving in the INBOX folder:

1. Email arrives in monitored account
2. EmailEngine extracts text content
3. Content sent to OpenAI API for analysis
4. Analysis results added to webhook payload

Webhook Enhancement

With AI processing enabled, messageNew webhooks include additional sections:

{
  "account": "example",
  "event": "messageNew",
  "data": {
    "id": "AAAAGQAACeE",
    "from": {
      "name": "Jane Doe",
      "address": "jane@example.com"
    },
    "subject": "Project meeting tomorrow at 2pm",
    "summary": {
      "id": "chatcmpl-7IzVIEp5UL3hdQ3aZJ8AHyrJrt3R0",
      "tokens": 245,
      "model": "gpt-4",
      "sentiment": "positive",
      "summary": "Request to attend project meeting tomorrow at 2pm in conference room A to discuss Q4 roadmap.",
      "shouldReply": true,
      "events": [
        {
          "description": "Project meeting",
          "startTime": "2023-06-07T14:00:00"
        }
      ],
      "actions": [
        {
          "description": "Attend project meeting",
          "dueDate": "2023-06-07"
        }
      ]
    },
    "riskAssessment": {
      "risk": 1,
      "assessment": "Sender information matches and authentication checks have passed."
    }
  }
}

Extracted Information

1. Content Summary

Condensed version of email content (sentence or short paragraph):

{
  "summary": "Request to contribute 2 to 5 euros for flower bouquets for choir teachers and concertmaster."
}

2. Sentiment Assessment

One-word sentiment evaluation:

• positive: Friendly, enthusiastic, grateful
• neutral: Informational, factual
• negative: Complaint, frustration, anger

{
  "sentiment": "positive"
}

3. Reply Expectation

Boolean flag indicating if sender expects a response:

{
  "shouldReply": true
}

4. Events List

Events with dates mentioned in the email:

{
  "events": [
    {
      "description": "Flower bouquets for choir teachers",
      "startTime": "2023-05-22"
    },
    {
      "description": "End of year celebration",
      "startTime": "2023-06-15",
      "endTime": "2023-06-15T18:00:00"
    }
  ]
}

5. Actions List

Tasks recipient is expected to perform:

{
  "actions": [
    {
      "description": "Contribute 2 to 5 euros for flower bouquets",
      "dueDate": "2023-05-22"
    },
    {
      "description": "RSVP for end of year celebration",
      "dueDate": "2023-06-10"
    }
  ]
}

6. Fraud Risk Assessment

Risk score from 1 to 5 (5 being highest risk) with explanation:

{
  "riskAssessment": {
    "risk": 4,
    "assessment": "Email contains urgent request for money transfer and sender domain doesn't match claimed identity. Possible phishing attempt."
  }
}

Note: AI is good at detecting scams but less effective with spam.

7. Reply/Forward Text Extraction

For reply emails, removes threaded content leaving only new text:

{
  "replyText": "Thanks for the update! I'll review the document and get back to you by Friday."
}

Metadata Storage

Token Usage

The tokens field shows OpenAI API tokens consumed:

{
  "summary": {
    "tokens": 2060,
    "model": "gpt-4"
  }
}

Use this to track API costs and usage.

Request ID

The id field contains the OpenAI request ID for troubleshooting:

{
  "summary": {
    "id": "chatcmpl-7IzVIEp5UL3hdQ3aZJ8AHyrJrt3R0"
  }
}

Custom Prompts

Customize the AI analysis by modifying the system prompt:

1. Go to Configuration → AI Processing
2. Scroll to AI Instructions section
3. Edit the AI Prompt
4. Add custom instructions
5. Save configuration

Example: Add Language Detection

Add this line to the prompt:

- Return the ISO language code of the primary language used in the email as the "language" property

Result in webhook:

{
  "summary": {
    "language": "en",
    "summary": "Meeting invitation for tomorrow at 2pm"
  }
}

Example: Custom Classification

Add business-specific classification:

- Classify the email type as "inquiry", "complaint", "order", or "other" in the "emailType" property

Result:

{
  "summary": {
    "emailType": "inquiry",
    "summary": "Customer asking about product availability"
  }
}

Handling Failures

EmailEngine skips AI processing if:

• OpenAI API request fails
• Rate limit exceeded
• Timeout occurs
• Email has no text content

In these cases, the summary section is omitted from webhook payload. Check EmailEngine logs for details.

Webhook Content Configuration

If webhooks are configured not to include email content, AI summarization may fail (nothing to summarize). Ensure webhook configuration includes at least text content.

Use Cases and Applications

1. Smart Email Routing

Route emails based on AI analysis:

// Pseudo code - implement in your preferred language
function routeEmail(webhook):
  summary = webhook.data.summary
  riskAssessment = webhook.data.riskAssessment

  if riskAssessment.risk >= 4:
    // High risk - route to spam/security team
    moveToSpam(webhook.data.id)
  else if summary.actions AND summary.actions.length > 0:
    // Has action items - create tasks
    createTasks(summary.actions)
  else if summary.shouldReply:
    // Needs response - add to priority inbox
    markAsPriority(webhook.data.id)

2. Automatic Calendar Events

Create calendar events from email mentions:

// Pseudo code - implement in your preferred language
function createEventsFromEmail(webhook):
  events = webhook.data.summary.events OR []

  for each event in events:
    if event.startTime:
      createCalendarEvent({
        title: event.description,
        start: event.startTime,
        end: event.endTime OR event.startTime,
        source: 'email',
        emailId: webhook.data.id
      })

3. Task Management Integration

Extract and create tasks:

// Pseudo code - implement in your preferred language
function createTasksFromEmail(webhook):
  actions = webhook.data.summary.actions OR []

  for each action in actions:
    createTask({
      title: action.description,
      dueDate: action.dueDate,
      source: webhook.data.from.address,
      emailSubject: webhook.data.subject,
      emailId: webhook.data.id
    })

4. Customer Support Triage

Automatically categorize and prioritize support emails:

// Pseudo code - implement in your preferred language
function triageSupportEmail(webhook):
  sentiment = webhook.data.summary.sentiment
  risk = webhook.data.riskAssessment.risk

  priority = 'normal'

  if sentiment == 'negative':
    priority = 'high'
  else if risk >= 3:
    priority = 'spam'
  else if webhook.data.summary.shouldReply:
    priority = 'normal'
  else:
    priority = 'low'

  assignToQueue(webhook.data, priority)

5. Email Analytics Dashboard

Build analytics from AI-processed emails:

// Pseudo code - implement in your preferred language
function trackEmailMetrics(webhook):
  summary = webhook.data.summary
  riskAssessment = webhook.data.riskAssessment

  // Track sentiment distribution
  incrementMetric('sentiment', summary.sentiment)

  // Track response requirements
  if summary.shouldReply:
    incrementMetric('requiresResponse')

  // Track action items
  if summary.actions AND summary.actions.length > 0:
    incrementMetric('hasActions', summary.actions.length)

  // Track high-risk emails
  if riskAssessment.risk >= 4:
    incrementMetric('highRisk')
    alertSecurityTeam(webhook.data)

6. Smart Email Search Assistant

Build conversational email search for users:

cURL:

curl -X POST "https://ee.example.com/v1/chat/user123" \
  -H "Authorization: Bearer <TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Did I receive the invoice from Acme Corp?"
  }'

Response:

{
  "success": true,
  "answer": "Yes, you received an invoice from Acme Corp on October 5th for $1,500.",
  "messages": [
    {
      "id": "AAAAGQAACeE",
      "from": {
        "name": "Acme Corp",
        "address": "billing@acmecorp.com"
      },
      "subject": "Invoice #12345",
      "date": "2023-10-05T10:00:00.000Z"
    }
  ]
}

Implementation (Pseudo code):

// Pseudo code - implement in your preferred language
function searchEmails(userId, question):
  response = HTTP_POST(
    url: "https://ee.example.com/v1/chat/" + userId,
    headers: {
      "Authorization": "Bearer " + token,
      "Content-Type": "application/json"
    },
    body: {
      "question": question
    }
  )

  data = parse_json(response.body)

  return {
    answer: data.answer,
    sourceEmails: data.messages
  }

// Usage example
result = searchEmails(
  userId: 'user123',
  question: 'Did I receive the invoice from Acme Corp?'
)

print(result.answer)
// Output: "Yes, you received an invoice from Acme Corp on October 5th for $1,500."

Privacy and Compliance

Data Processing

Important: When OpenAI integration is enabled, EmailEngine uploads email text content to OpenAI servers.

OpenAI Policy: OpenAI does not use API data for training models.

Your Responsibility: Verify this behavior complies with:

• User data processing agreements
• GDPR requirements
• Industry-specific regulations (HIPAA, etc.)
• Company privacy policies

User Consent

Recommendations:

1. Transparent Disclosure: Inform users that AI processes their emails
2. Opt-In: Allow users to enable/disable AI processing
3. Data Retention: Clarify how long AI-processed data is stored
4. Third-Party Processing: Disclose data sent to OpenAI

Per-Account Control

You can enable/disable AI processing per account if needed (would require custom implementation).

Cost Management

Estimating Costs

OpenAI charges based on token usage:

GPT-3.5 Turbo (as of 2023):

• Input: $0.0015 per 1K tokens
• Output: $0.002 per 1K tokens

GPT-4:

• Input: $0.03 per 1K tokens
• Output: $0.06 per 1K tokens

Example: Processing 100 emails per day with GPT-3.5:

• Average email: ~500 tokens input, 200 tokens output
• Daily tokens: ~70,000 tokens
• Monthly cost: ~$3-5

Cost Optimization

1. Use GPT-3.5 for Summaries: Reserve GPT-4 for complex analysis
2. Filter Emails: Only process emails from Inbox (skip spam, notifications)
3. Selective Processing: Process only emails matching certain criteria
4. Monitor Usage: Track tokens field in webhooks
5. Rate Limiting: Limit processing during high-volume periods

Monitoring Token Usage

Track token usage from webhooks:

// Pseudo code - implement in your preferred language
function trackTokenUsage(webhook):
  if webhook.data.summary exists:
    tokens = webhook.data.summary.tokens
    model = webhook.data.summary.model

    logMetric('openai_tokens', {
      model: model,
      tokens: tokens,
      account: webhook.account,
      timestamp: current_timestamp()
    })