Training course · Make · Engineering

Make for Engineers

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 Engineering. 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.

Engineers in design, development, plant engineering and technical planning spend much of their time on research, documentation, coordination and calculations. Generative AI can reduce that share considerably: AI assistants summarise standards and technical literature, structure requirement and design specifications, support requirements analysis, generate calculation scripts in Python or spreadsheets and produce technical documentation and reports. Automation helps with change management, comparing document revisions and analysing test and measurement data. Creative problem-solving, plausibility checks and responsibility for technical safety remain with the experts. Using AI for routine work and as a sparring partner frees time for real engineering: developing concepts, evaluating variants and delivering robust solutions.

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?

  • Design and development engineers
  • Project engineers in plant and machine building
  • Test and validation engineers
  • Technical writers
  • Engineers in planning and engineering consultancies

Typical challenges we address

  • Evaluating extensive standards, guidelines and requirements efficiently
  • Producing technical documentation and reports alongside development work
  • Analysing and interpreting test and measurement data quickly
  • Managing changes traceably across many documents and stakeholders

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.
  • Use AI for requirements analysis, standards research and technical documentation.
  • Have AI create calculation and analysis scripts and validate them systematically.
  • Protect engineering know-how and observe product safety and liability requirements when using AI.

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: Engineering
  • Structuring requirements
  • Standards and literature review
  • Calculation scripts
  • Test data analysis
  • Technical documentation
  • Change management support
Module 7 Data protection, quality & transfer to daily work
  • Design data, drawings and development results are trade secrets and may be subject to export control; they belong only in approved AI environments. Product safety and liability law apply unchanged to safety-related designs, so AI output must be verified and documented traceably.
  • 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

Structuring requirements

An AI assistant analyses customer requirements and specifications, spots contradictions, gaps and vague wording and turns them into a structured, verifiable requirements list for the design specification.

Standards and literature review

AI summarises relevant sections of standards, guidelines and technical papers, creates comparison tables and checklists and references the passages, which engineers check in the original.

Calculation scripts

Instead of coding formulas by hand, describe the calculation and receive a commented Python script or spreadsheet, for example for sizing, tolerance stack-ups or unit conversions, ready for testing and validation.

Test data analysis

Test bench data is cleaned, statistically analysed and visualised using AI-generated analysis code, anomalies are flagged and a draft test report is created.

Technical documentation

Design data, notes and previous documents are turned into drafts of operating manuals, maintenance handbooks, specifications and project reports with consistent terminology.

Change management support

AI compares document revisions, summarises technical changes clearly, identifies affected documents and interfaces and prepares change requests for the change board.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. You have an engineering or technical background and work in development, design, planning or testing.

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

Design data, drawings and development results are trade secrets and may be subject to export control; they belong only in approved AI environments. Product safety and liability law apply unchanged to safety-related designs, so AI output must be verified and documented traceably.

Suggested dates

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

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Stuttgart Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Berlin Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Online Request
  • Wed, 10 February 2027 – Thu, 11 February 2027 Munich 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.

Can I use AI calculations for safety-related designs?

Only as support with full verification. Language models can get formulas, units or standard values wrong. Have calculation steps output as traceable code or formulas, check them against standards, hand calculations or validated software and document the check. Responsibility remains with you.

How do we protect design data?

Use only approved enterprise environments where inputs are not used for training. Also observe export control rules, for example for dual-use technologies, and agree with IT and export control which information may go into which tools. We discuss practical rules in the course.

How much does “Make for Engineers” 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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