AI Integration Intermediate

AI Integration 101: Adding AI to Your Workflows

Learn how to add AI capabilities like GPT-4 and Claude to your automation workflows.

AI Integration Intermediate Updated July 27, 2026

AI Integration 101

AI transforms automation from rule-based to intelligent. Let us learn how.

What AI Brings

Without AI

Without AI: IF email subject contains urgent THEN notify manager. Hard to handle variations.

With AI

With AI: AI reads email and understands urgency level. Determines action based on context. Learns from feedback.

Available AI Models

GPT-4

Best for: General tasks, text analysis, summarization
Cost: Approximately 0.03 dollars per 1000 tokens
Speed: 1-5 seconds per request
Integration: Direct API or through Octacer UI

Claude

Best for: Complex reasoning, analysis, code review
Cost: Approximately 0.01 dollars per 1000 tokens
Speed: 2-8 seconds per request
Integration: Through Octacer

Llama

Best for: Privacy-sensitive, self-hosted deployments
Cost: Free (self-hosted) or 0.001-0.005 dollars per token
Speed: Variable depending on setup
Integration: Custom deployment needed

Adding AI to a Workflow

Example: Intelligent Email Classification

Goal: Read incoming emails and automatically classify them as sales inquiry, support request, partnership proposal, or spam.

Workflow:

  1. 1

    Build Workflow

    Trigger: New email arrives in inbox
    Extract: Get email subject and body
    AI Step: Classify this email into sales, support, partnership, or spam
    Condition: Based on classification, route to different team
    Action: Move to appropriate folder and notify team member

  2. 2

    Configure AI

    Create new workflow Email Classification
    Add trigger: Gmail - New Email
    Add action: AI - Classification
    Configure AI step:

  3. 3

    Publish

    Add conditional routing
    Test with real emails
    Publish

Setup:

  • Model: GPT-4
  • Prompt: Classify this email. Email: {subject} {body}
  • Output format: JSON with category and confidence

AI Capabilities Available

Text Analysis

Sentiment analysis (positive, negative, neutral)
Topic extraction
Keyword identification
Language detection

Content Generation

Email drafts
Social media posts
Document summaries
Report generation

Data Processing

Extract structured data from unstructured text
Validate and clean data
Format conversion
Data enrichment

Decision Making

Routing decisions
Priority scoring
Risk assessment
Opportunity identification

Best Practices

DO:

Do

Start with simple use cases
Test thoroughly before deploying
Monitor costs and results
Use AI for decision support, not final authority
Set confidence thresholds

Don't

Use AI for every step
Deploy without testing
Ignore edge cases
Assume AI is always right
Forget about compliance and privacy

DON'T:

Getting Help

  • Check AI Model Comparison guide for detailed specs
  • Watch AI Workflow Tutorial video
  • Ask in community Slack number ai-integration channel
  • Contact support for complex custom AI needs

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