Training course · n8n · Pharma & Life Sciences

n8n for Pharma and Life Sciences

Workflow automation with n8n and AI: master nodes, the AI Agent node, integrations and GDPR-friendly self-hosting in practice.

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

“n8n for Pharma and Life Sciences” is a hands-on course for roles such as Regulatory affairs professionals, Quality assurance and Qualified Persons and Clinical operations and medical writers. Rather than learning n8n in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Pharma & Life Sciences.

n8n is a flexible workflow automation platform that combines visual flows with language models and hundreds of integrations. Unlike many cloud services, n8n can also be self-hosted, so your data can stay in your own data centre or with a European hosting provider. Over two days you will learn how triggers, nodes and data flows fit together, how to transform data between applications and how to automate recurring tasks from your day-to-day work reliably.

The core of the course is the combination of automation and AI. Using the AI Agent node, chat models, memory and tools, you build workflows that classify emails, summarise documents, enrich records or answer enquiries. You learn to catch errors, test workflows and document them properly. Finally, you look at hosting options, credential management and data protection, so that your automations run stably, securely and in line with GDPR once they go into everyday operation.

Pharmaceutical, biotech and medtech companies operate in one of the most heavily regulated environments of all. Documentation, validation and traceability shape daily work from research and clinical trials to manufacturing. Generative AI offers significant potential: AI assistants review scientific literature, help draft study protocols, regulatory documents and SOPs, summarise deviation reports, support pharmacovigilance case processing and prepare medical information for healthcare professionals. Automation supports change control, CAPA tracking and regulatory intelligence. At the same time, as soon as AI touches GxP-relevant processes, validation, data integrity according to ALCOA+ and clear accountability are mandatory. Adoption succeeds when companies clearly separate non-regulated support from GxP use and involve quality assurance early.

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?

  • Regulatory affairs professionals
  • Quality assurance and Qualified Persons
  • Clinical operations and medical writers
  • Pharmacovigilance and drug safety teams
  • Medical affairs and R&D scientists

Typical challenges we address

  • Producing extensive regulatory and scientific documents to a high standard
  • Processing deviations, CAPAs and change controls on time
  • Systematically evaluating growing volumes of literature and regulation
  • Using AI tools in a GxP-compliant, validatable way

What you will be able to do

  • You can build n8n workflows independently using triggers, logic nodes and data transformations.
  • You can connect n8n to the applications you use every day and fall back on the HTTP Request node where needed.
  • You can use the AI Agent node and other AI building blocks for classification, extraction and summarisation.
  • You can safeguard workflows with error handling, validation and human approval steps.
  • Use AI for literature review, medical writing and regulatory intelligence.
  • Distinguish non-regulated AI use from GxP-relevant applications and understand validation requirements.
  • Safeguard data integrity, confidentiality and patient data protection when using AI.

Course outline

Day 1

Module 1 Fundamentals: workflows, triggers and nodes
  • Getting to know the n8n interface, canvas and execution view
  • Trigger types: schedules, webhooks, incoming email and app events
  • Understanding items and JSON data and working with expressions
  • Using core nodes such as Edit Fields, IF, Switch, Merge and Loop Over Items
Module 2 Integrations and data processing
  • Connecting common applications: email, spreadsheets, CRM, collaboration and cloud storage
  • Using the HTTP Request node for APIs without a ready-made integration
  • Creating, managing and sharing credentials securely within the team
  • Filtering, merging and reshaping data and writing it to target systems
Module 3 AI in n8n: the AI Agent node and LLM building blocks
  • Connecting chat models from different providers as well as locally hosted models
  • Configuring the AI Agent node with a system prompt, memory and tools
  • Automating classification, structured data extraction and summarisation
  • Using your own documents via vector stores for retrieval-augmented generation
Module 4 Error handling, testing and maintainability
  • Setting up error workflows, retries and failure notifications
  • Validating AI output and defining fallbacks for unusable responses
  • Working with pinned test data and tracing past executions
  • Modularising, naming and documenting workflows for colleagues

Day 2

Module 5 Self-hosting, data protection and operations
  • Cloud versus self-hosted: effort, control and licensing model compared
  • Assessing data flows to AI providers against GDPR requirements
  • Managing access rights, logging and retention of execution data
  • Adding human-in-the-loop approval steps to automated processes
Module 6 Practice lab: Pharma & Life Sciences
  • Literature review and evidence
  • Medical writing support
  • SOPs and training materials
  • Deviations and CAPA
  • Regulatory intelligence
  • Pharmacovigilance support
Module 7 Data protection, quality & transfer to daily work
  • When AI systems are used in GxP-relevant processes, computerised system validation under EU GMP Annex 11 or 21 CFR Part 11 and data integrity according to ALCOA+ must be ensured. Clinical trial and pharmacovigilance data contain health data under Article 9 GDPR and may only be processed in pseudonymised form in approved 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

Literature review and evidence

An AI assistant summarises scientific publications, extracts study design, endpoints and results into tables and supports systematic reviews, with experts checking every data point against the original source.

Medical writing support

AI creates outlines and first drafts of study protocols, clinical study reports, investigator brochures or modules of the regulatory dossier based on approved templates and source data.

SOPs and training materials

Process descriptions and regulations are turned into draft SOPs, training slides and comprehension questions, which are then reviewed and approved through the validated document management system.

Deviations and CAPA

AI summarises deviation reports, structures root cause analyses, finds similar past cases and suggests CAPAs, which quality assurance evaluates and decides on.

Regulatory intelligence

Automations collect new guidelines and publications from the EMA, national authorities or the FDA; AI summarises changes and maps them to affected products and processes for an impact assessment.

Pharmacovigilance support

AI helps pre-structure case reports from unstructured text, extract relevant information on product, event and patient and detect duplicates, while medical assessment remains with qualified staff.

Prerequisites

You should be confident with common office applications and have basic experience with AI assistants. No programming is required, although a basic grasp of structured data is helpful. You work in the pharmaceutical, biotech or medtech industry and know the GxP basics of your area.

Technical requirements

You will need a laptop with an up-to-date browser. We provide an n8n environment for the exercises, or you can use your own instance; API keys for a language model are set up together during the course.

Data protection & compliance

When AI systems are used in GxP-relevant processes, computerised system validation under EU GMP Annex 11 or 21 CFR Part 11 and data integrity according to ALCOA+ must be ensured. Clinical trial and pharmacovigilance data contain health data under Article 9 GDPR and may only be processed in pseudonymised form in approved environments.

Suggested dates

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

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Mon, 30 November 2026 – Tue, 1 December 2026 Kaiserslautern Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Online Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Augsburg Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Salzburg Request

Frequently asked questions

Do I need to code to work with n8n?

No. Most workflows are built visually by connecting nodes. During the course you also learn simple expressions to reference and reshape data precisely. A Code node is available for special cases but is not essential. A logical understanding of processes and data fields is entirely sufficient to get started.

Why is n8n attractive from a European data protection perspective?

n8n can be self-hosted, for instance on your own servers or with a European hosting provider. This keeps you in control of where workflow and execution data is stored. What still matters is which data you send to external AI services. On the course you learn to assess these data flows and, where appropriate, connect locally hosted models.

How does n8n differ from Make or Power Automate?

All three automate processes across applications. n8n stands out for its self-hosting option, its fair-code licensing and particularly flexible AI building blocks such as the AI Agent node. Make offers a very approachable cloud interface, while Power Automate excels at deep Microsoft integration. The course puts these differences into context for typical scenarios.

Do AI tools need to be validated in pharma?

It depends on the use. For activities without GxP relevance, such as internal communication or literature overviews, general IT and data protection rules apply. If results feed into GxP-relevant decisions or records, a risk-based validation approach is required. We show you how to classify use cases.

How do we ensure data integrity?

AI output must be attributable, traceable and verifiable. That means documented inputs and prompts, clearly assigned reviewers, versioning and release through your validated systems. In the course we discuss how ALCOA+ principles apply to AI-supported work steps. Clear retention rules complete the picture.

How much does “n8n for Pharma and Life Sciences” 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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