No-Code Automation | Enablers

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No-Code Automation

Sessions Venue
Online & On Campus Apply Now

Benefits to join Boot Camp

2 Months of Detail Sessions (Face to Face & Online)

Enablers Trainer Support Program (EMS)

Dedicated Student Support on Private Facebook Group

The Upgraded Private Enablers Community

Course Overview

  • Imagine automating your most repetitive tasks, connecting your favourite apps, and even building AI-powered workflows — all without writing a single line of code. No-Code Automation is a comprehensive, hands-on programme designed for professionals, business owners, and operations teams who want to harness the full power of AI and automation tools to work smarter, faster, and more efficiently.
  • Across 8 practical modules, you will move from understanding how AI language models work to building real, production-ready automated workflows using industry-leading platforms including n8n, Make.com, Zapier, Claude, ChatGPT, and Supabase. You will learn how to connect apps, process data, call AI APIs, build desktop agents, and even create autonomous multi-step agents — all through visual, no-code interfaces.

Learning Outcomes

  • Build real automated workflows
  • Integrate AI into your business processes
  • Work with desktop agents
  • Automate repetitive operations tasks
  • Build and deploy production-ready agents

Module Breakdown

  • Inside a Language Model
    • What a token is and how models predict the next token
    • The difference between a base model and an instruction-tuned chat model
    • Why the same prompt can produce different outputs across runs
    • Context windows and why long inputs get truncated or summarized
  • Working with ChatGPT, Claude, and Gemini
    • Comparing ChatGPT, Claude, and Gemini for everyday operational tasks
    • Choosing a model based on context length, reasoning, and cost
    • Switching between models without rewriting your prompts
    • Saving and reusing prompts as repeatable building blocks
    • checks before any AI output reaches a client or executive
  • Reliable Output for Operations
    • Asking for output in a fixed shape (lists, tables, JSON)
    • Setting role, task, and constraints in a single prompt
    • Catching when a model invents facts versus reports them
  • Structured Context for Repeatable Results
    • Separating instructions, data, and examples in a prompt
    • Providing reference data so the model answers from your content
    • Using delimiters and field labels to keep inputs unambiguous
    • Few-shot examples to lock in tone and formatting
    • Templating prompts so non-technical staff can reuse them
  • Accuracy and Privacy Basics
    • Recognizing hallucinations and adding verification steps
    • What data should and should not be sent to a model
    • Redacting personal and customer data before a request
    • Retention and training settings across ChatGPT, Claude, and Gemini
  • The Vocabulary of Automation
    • Triggers: what starts a workflow (a new email, a form submission, a schedule)
    • Actions: what a workflow does (create a record, send a message)
    • Filters and conditions: deciding when steps should run
    • Payloads: the data that moves between steps
    • Mapping these terms to real operations tasks before touching a tool
  • API Thinking Without Code
    • What an API endpoint, request, and response actually are
    • Reading API documentation to find the fields you need
    • Authentication with API keys and where to store them safely
    • Understanding rate limits, errors, and retries at a high level
  • Calling the OpenAI API and Claude API
    • Making an HTTP request to the OpenAI API from a no-code tool
    • Making an HTTP request to the Claude API from a no-code tool
    • Sending a system prompt, user message, and structured output request
    • Parsing the response and pulling out only the fields you need
    • Handling cost and token usage per request
  • Connecting AI Into a Workflow
    • Passing data from a trigger into an AI request
    • Using AI output as the input to the next step
    • Logging requests and responses for review
  • Claude Cowork as a Desktop Agent
    • What an agentic desktop AI does versus a chat assistant
    • Letting Claude Cowork read local files and folders
    • Driving desktop apps to complete multi-step tasks
    • Reviewing and approving the actions an agent proposes
    • Practical operations tasks: sorting files, drafting documents, updating sheets
  • OpenClaw for Local Automation
    • Setting up OpenClaw as a desktop agent
    • Comparing OpenClaw and Claude Cowork for local tasks
    • Choosing a desktop agent versus a cloud workflow
  • Vibe Coding with Claude Code
    • When a no-code integration needs a small piece of glue code
    • Describing the problem in plain language to Claude Code
    • Letting Claude Code write, run, and fix the snippet for you
    • Pasting the generated code into a no-code tool without editing it by hand
    • Building a first end-to-end workflow that combines AI, an API call, and an action
  • Building Multi-Step Flows
    • The canvas model: nodes, connections, and execution order in n8n and Make.com
    • Setting up a trigger and chaining several actions
    • Mapping fields between steps and previewing data at each node
    • Running, testing, and re-running a flow with sample data
  • Branching, Filters, and Formatters
    • Routing a flow down different paths with conditions
    • Filtering out records that should not continue
    • Formatting dates, text, and numbers between steps
    • Doing lookups to enrich a record from another source
    • Handling errors so one bad item does not stop the flow
  • Integrations Across Your Stack
    • Connecting a CRM, email, and spreadsheets in one flow
    • Reading and writing rows in Google Sheets and Airtable
    • Comparing Zapier, Make.com, and n8n for a given task
    • Moving data into Supabase as a shared store
  • Working in n8n Cloud
    • Getting started in n8n Cloud with no server setup required
    • Managing credentials and connections securely
    • Versioning, exporting, and importing workflows for backup and reuse
    • Organizing workflows as a project grows
  • Advanced Workflow Patterns
    • Looping over lists of items
    • Splitting and merging data across branches
    • Calling the OpenAI API and Claude API directly from n8n nodes
    • Scheduling flows and reacting to webhooks
    • Building reusable sub-workflows
  • Reliability and Monitoring
    • Reading execution logs to debug a failed run
    • Adding retries and fallback paths
    • Alerting a human when a flow fails
  • From Workflow to Autonomous Agent
    • The difference between a fixed workflow and a multi-step agent
    • Giving an agent a goal, tools, and a stopping condition
    • Letting an agent decide which action to take next
    • Combining AI reasoning with n8n or Make.com actions
  • Supabase as a Data Layer
    • Using Supabase tables to store agent state and history
    • Reading and writing records from a workflow
    • Querying past runs to give an agent memory
    • Keeping a clean audit trail of every decision
  • Guardrails and Human-in-the-Loop
    • Setting limits on what an agent is allowed to do
    • Confidence thresholds that route uncertain cases to a person
    • Approval checkpoints before an action is committed
    • Validating AI output before it writes to a system of record
  • Deploying and Handing Over
    • Moving a flow from test to production safely
    • Documenting triggers, actions, and credentials for owners
    • Writing a runbook a non-technical owner can follow
    • Setting up monitoring and alerts before handover
    • Planning for changes when an upstream tool updates
  • Capstone: A Support Triage Agent
    • Reading incoming emails as the trigger
    • Classifying each message by type and urgency with the Claude API or OpenAI API
    • Drafting a reply for review
    • Logging the case and outcome to the CRM and Supabase
    • Escalating low-confidence cases to a human checkpoint
  • Review and Handover of the Capstone
    • Testing the full flow with real sample data
    • Documenting the build for the operations owner
    • Identifying the next workflow to automate