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    Lesson 3 of 5

    Visual AI Workflows with Make

    From the course AI-Powered Automation: Zapier, Make & n8n

    Make (formerly Integromat) offers a visual workflow builder that's perfect for complex, multi-step AI automations with branching logic.

    Make's Visual Advantage

    Make displays workflows as visual flowcharts, making it easy to:

    • See the entire automation at a glance
    • Add complex branching and routing logic
    • Handle errors with dedicated error paths
    • Process data in parallel branches

    Make AI Modules

    • OpenAI (ChatGPT) — Send prompts, generate images, create embeddings
    • Anthropic (Claude) — Text generation and analysis
    • HTTP module — Connect to any AI API (Gemini, Mistral, etc.)
    • JSON module — Parse AI responses into structured data

    Pattern 1: Customer Feedback Analysis Pipeline

    New survey response (Typeform) → OpenAI: "Analyze this feedback. Return JSON: {sentiment, topics: [], urgency, suggested_action, summary}" → JSON Parse: Extract structured data → Router: → Branch 1 (negative + urgent): Create Jira ticket + alert manager on Slack → Branch 2 (negative + not urgent): Add to feedback spreadsheet + schedule follow-up → Branch 3 (positive): Add testimonial to database + thank the customer

    Pattern 2: Document Processing

    New file in Google Drive → Google Docs: Extract text → OpenAI: "Summarize this document in 3 bullet points. Extract: key dates, action items, and people mentioned. Return as JSON." → Google Sheets: Add structured data to tracking sheet → Slack: Post summary to relevant channel

    Pattern 3: Multi-Language Support

    New support ticket (Zendesk) → OpenAI: "Detect the language of this message. Translate to English if not English. Classify the issue type. Draft a response in the original language." → Zendesk: Add internal note with English translation and classification → Zendesk: Send AI-drafted response (if auto-reply is enabled)

    Advanced Make Techniques

    • Iterators — Process arrays from AI responses one element at a time
    • Aggregators — Combine multiple AI results into a single output
    • Data stores — Cache AI results for deduplication and reference
    • Webhooks — Trigger AI workflows from any external event
    • Scheduling — Run AI analysis on schedules (daily reports, weekly summaries)

    Key Takeaways

    • •Make's visual builder is ideal for complex, branching AI workflows
    • •Use the Router module for intelligent routing based on AI analysis
    • •Parse AI JSON responses with the JSON module for structured data handling
    • •Multi-language support: detect, translate, classify, and respond in one workflow
    • •Advanced features: iterators, aggregators, data stores, and scheduled execution
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