Training course · Make · Quality Management

Make for Quality Management Teams

Automation with Make (formerly Integromat) and AI modules: build scenarios, routers and AI steps while keeping data protection in check.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Make with the everyday processes of Quality Management. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Make, formerly known as Integromat, is a visual automation platform that lets you connect applications and automate recurring processes without writing code. Over two days you will learn to build scenarios systematically, from triggers and modules through routers and filters to iterators, aggregators and data stores. You will understand how data flows through a scenario, how to reshape it with functions and how to keep an eye on operations consumption.

The second focus is AI. You connect language models through Make’s AI modules and provider integrations to classify text, extract information from emails and documents, draft replies or enrich content. You learn to catch errors with error handlers, test scenarios and document them clearly. Finally, you address data protection topics such as hosting region, data processing agreements and data minimisation, so that your automations can be run safely within your organisation.

Quality management depends on clean documentation, systematic analysis and rigorous follow-up of actions. Generative AI can relieve quality teams exactly there: AI assistants create and revise procedures, prepare audits, analyse complaints, support 8D reports and FMEA and compare documents with standard requirements such as ISO 9001 or IATF 16949. Automation sends reminders about action deadlines, evaluates inspection data and consolidates supplier ratings. That leaves more time for root cause work, process improvement and collaboration with production and engineering. AI output must not replace conformity with standards; it needs expert review and must enter the management system through controlled processes. When document control and approval workflows are respected, AI becomes a reliable tool for quality and efficiency.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Quality managers and management representatives
  • Quality engineers and quality planners
  • Internal auditors
  • Complaint and supplier quality managers
  • Process owners in management systems

Typical challenges we address

  • Keeping extensive quality documentation current, consistent and compliant with standards
  • Analysing and resolving complaints and nonconformities quickly
  • Preparing internal and external audits efficiently
  • Reliably tracking actions from audits, 8D reports and management reviews

What you will be able to do

  • You can build Make scenarios independently using triggers, routers, filters and data transformations.
  • You can process lists and records with iterators, aggregators and data stores.
  • You can integrate AI modules for classification, extraction and text generation into your processes.
  • You can safeguard scenarios with error handlers, validation and human approval.
  • Create and maintain quality documentation efficiently and in line with standards using AI.
  • Use AI for complaint analysis, 8D, FMEA and audit preparation.
  • Integrate AI output into your management system in a controlled and traceable way.

Course outline

Day 1

Module 1 Fundamentals: scenarios, modules and data flow
  • Interface layout, scenario editor and execution history
  • Triggers, actions and searches: module types and their role in a scenario
  • Understanding bundles, mapping and data types and using them deliberately
  • Choosing between schedules, webhooks and instant triggers
Module 2 Logic: routers, filters, iterators and aggregators
  • Using routers and filters for branching and conditional flows
  • Splitting lists with iterators and combining them with aggregators
  • Applying text, date and maths functions to transform data
  • Using data stores and variables for interim values and duplicate checks
Module 3 AI building blocks in Make
  • Connecting language models via AI modules and provider integrations
  • Automating classification, structured data extraction and text generation
  • Building prompts from scenario variables and processing output as JSON
  • Combining AI agents in Make with tools and scenarios as actions
Module 4 Integrations, error handling and testing
  • Connecting common business applications and any API via the HTTP module
  • Applying error handlers such as Resume, Ignore, Break and Rollback
  • Validating AI output and routing unusable results to a person
  • Testing scenarios, optimising operations and documenting them for the team

Day 2

Module 5 Data protection, governance and operations
  • Assessing hosting region, data processing agreements and data flows to AI providers
  • Applying data minimisation and pseudonymisation within scenarios
  • Managing connections, teams and permissions across the organisation
  • Defining approval steps, monitoring and ownership for live operation
Module 6 Practice lab: Quality Management
  • Writing procedures
  • Standards gap analysis
  • 8D report support
  • FMEA preparation
  • Audit preparation
  • Complaint data analysis
Module 7 Data protection, quality & transfer to daily work
  • AI-generated documents must meet the standard's requirements for controlling documented information and be reviewed by experts before release. Customer data from complaints and confidential product information may only be processed in approved, GDPR-compliant AI environments.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Writing procedures

An AI assistant turns process descriptions and interviews into procedures and work instructions with a consistent structure, checks them for contradictions with other documents and suggests improvements.

Standards gap analysis

AI compares existing documents with the requirements of ISO 9001, IATF 16949 or ISO 13485, highlights possible gaps and produces an action list for expert evaluation.

8D report support

For complaints, AI structures the problem description, suggests containment actions and possible root causes and writes the 8D report in a form customers can understand.

FMEA preparation

Based on process steps and experience, AI suggests failure modes, effects and causes and helps the team facilitate and document FMEA sessions.

Audit preparation

From standard clauses and the process map, AI creates audit questions, checklists and audit plans and condenses audit notes into a structured draft report with findings.

Complaint data analysis

Complaints and inspection results are analysed by defect type, product and supplier, trends identified and priorities for improvement projects proposed.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. You work in quality management and know the requirements of your management system, for example ISO 9001.

Technical requirements

You will need a laptop with an up-to-date browser and a Make account; the free plan covers most exercises. API access for a language model is set up together during the course.

Data protection & compliance

AI-generated documents must meet the standard's requirements for controlling documented information and be reviewed by experts before release. Customer data from complaints and confidential product information may only be processed in approved, GDPR-compliant AI environments.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Bremen Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Salzburg Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Online Request
  • Mon, 8 February 2027 – Tue, 9 February 2027 Kaiserslautern Request
  • Wed, 17 February 2027 – Thu, 18 February 2027 Online Request

Frequently asked questions

Is Make the same as Integromat?

Yes. Integromat was renamed Make and substantially developed along the way. The core principle of visual scenarios made of connected modules remains, while a redesigned interface, many new integrations and AI building blocks have been added. If you used Integromat in the past, you will quickly feel at home and discover the new capabilities on the course.

How do I keep operation costs under control?

Every module that runs consumes operations or credits, and AI calls also use quota with the respective model provider. On the course you learn to design scenarios so that filters apply early, unnecessary runs are avoided and data is processed in batches. This keeps your automations economical and predictable as volumes grow.

Can Make be used in a GDPR-compliant way?

In principle yes, provided you set up the framework deliberately. This includes choosing the hosting region, signing a data processing agreement, reviewing connected AI services and applying data minimisation. On the course you work through these points with a checklist and learn to filter or pseudonymise sensitive data within scenarios.

Do auditors accept AI-generated documents?

What matters is not who drafted a document but whether it is technically correct, controlled, approved and applied in practice. If you review AI drafts, release them through your document control and keep responsibility clear, they meet the standard's requirements. We show how to integrate AI cleanly into these processes.

Can AI replace an FMEA?

No. An FMEA relies on the team's cross-functional knowledge of product and process. AI can prepare, make suggestions and document, which makes sessions more efficient. Ratings and decisions on actions remain with the team, which is accountable for the result.

How much does “Make for Quality Management Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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