How a Milan Engineering Team Built a Regulator-Ready Evidence Trail with Automated AI Technical Documentation Pack

In today’s rapidly evolving regulatory environment, compliance with the EU AI Act is essential for engineering teams deploying AI systems. 🚀 A specialized engineering team in Milan faced the challenge of generating and maintaining a comprehensive, regulator-ready evidence trail aligned with this new legislation. This case study details how they leveraged automation to build an AI technical documentation pack, transforming manual, error-prone tasks into scalable, auditable workflows.

In this article, you will learn how RestFlow helped create a compliance-first automation strategy, integrating key tools and designing workflows that ensure risk management, transparency, human oversight, technical documentation, and traceability. We will explore the technical architecture, workflow nodes, and real-world results, offering startup CTOs, automation engineers, and operations specialists a practical guide to automating compliance under EU regulations.

The Problem: Compliance Friction under the EU AI Act

The client is an engineering team based in Milan, Italy, specializing in AI system design and deployment within the broader AI research and product development sector. Due to the EU AI Act, their AI solutions require extensive technical documentation, risk assessments, transparency measures, and human oversight evidence. Before automation, the team relied heavily on manual documentation efforts, including:

• Creating and updating AI model logs and version histories in spreadsheets.
• Manually tracking changes, decisions, and risk mitigation steps via disconnected tools like emails and shared drives.
• Generating compliance reports under tight deadlines, often with incomplete data or inconsistencies.

This manual process consumed approximately 25 hours per month with an estimated 15% error rate in records, causing delays in audits and exposing operational risk. The lack of streamlined traceability and transparency hindered internal governance and increased anxiety around regulatory inspections.

Our Approach: Designing an Automation Strategy with RestFlow

RestFlow’s team began with a discovery workshop in Milan, involving AI engineers, compliance officers, and operations leads. The goal was to map the entire technical documentation lifecycle, pinpointing critical data flows and pain points.

Key systems were identified including:

• Google Sheets – currently used for preliminary tracking.
• Slack – primary communication channel for dev and ops teams.
• Google Drive – storage for project documents and manuals.
• Jira – issue and task tracking for change management.

Given the flexibility and extensive integrations offered, the team chose n8n as the orchestration tool within RestFlow’s Automation-as-a-Service platform to automate workflows. n8n’s open-source architecture allowed custom connectors and secure handling of sensitive data, ideal for regulatory workloads.

A high-level automation architecture was drafted featuring triggered data capture, risk evaluation logic, technical document generation, logging, and alerting – all designed to ensure compliance with the EU AI Act’s themes of risk management, transparency, human oversight, and traceability.

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Solution Architecture & Workflow

The final automation solution is composed of the following main components:

• Trigger: Scheduled workflows and Jira webhook triggers initiate documentation updates and change log captures.
• Orchestration Tool: n8n hosted and monitored by RestFlow, acting as the workflow engine.
• External Services: Google Sheets for structured data entry; Google Drive for document storage and version control; Slack for notifications and human oversight approvals; Jira API for change tracking.
• Outputs: Regulator-ready PDF technical documentation packs, audit dashboards on Google Sheets, and alerts for outstanding risks or missing approvals.

End-to-End Workflow Overview

1. Trigger Reception: A Jira webhook fires on issue updates related to AI system changes or risk assessments.
2. Data Collection & Validation: n8n fetches relevant data from Jira, Google Sheets, and Drive, validating completeness.
3. Risk Analysis: Branch logic evaluates current risks against thresholds defined by the compliance officers.
4. Human Oversight: If risks exceed limits, automated Slack messages notify designated reviewers to approve or request revisions.
5. Documentation Generation: Upon approval, n8n compiles logs, change history, and risk mitigation evidence into PDF documentation.
6. Storage & Traceability: PDFs are stored in structured folders in Google Drive, with metadata logged in Google Sheets.
7. Audit Reporting: Summary dashboards update live with compliance status and key metrics.

Step-by-Step Node Breakdown in n8n

1. Jira Webhook Trigger 📩

This node listens for incoming webhooks from Jira when issues tagged with “AI System” updates occur. Inputs include issue ID, update type, and timestamp.
Key fields: Issue key, updated fields, user performing the change.
This trigger keeps the workflow reactive and real-time.

2. Google Sheets Lookup & Validation 🔎

Reads corresponding rows from the central AI documentation spreadsheet. Validates that risk entries and change logs are complete.
Logic: If mandatory fields are missing, a Slack alert is generated prompting action.

3. Risk Management Decision Branch ⚠️

Evaluates current risk scores against predefined limits.
If true: Proceeds to human review.
If false: Directly compiles documentation.

4. Slack Approval Request & Reminder ⏳

Sends a formatted message tagging reviewers with inline buttons for approve/reject.
Input: Risk details and change summary.
Output: User response captured for workflow branching.

5. PDF Documentation Generation 📄

Assembles all logs, validated data, and approval records into a PDF via Google Docs API integration.
Includes version history table and links to supporting files.

6. Google Drive Storage & Metadata Logging 🗂️

Uploads the PDF into the client’s dedicated compliance folder with timestamped filename.
Logs metadata back into Google Sheets for traceability and audit purposes.

7. Compliance Dashboard Update 📊

The script pushes aggregate data (number of documents generated, pending approvals, risk scores) into a dashboard sheet for real-time management visibility.

Error Handling, Robustness & Security

Robustness is built into the workflow with these features:

• Retries: Failed API calls automatically retried with exponential backoff.
• Idempotency: Duplicate triggering is avoided using Jira issue timestamps and unique execution IDs stored in Google Sheets.
• Error Notifications: Detailed Slack alerts notify admins of persistent issues requiring manual intervention.
• Security: API keys and tokens are stored securely in n8n’s credential vault with scoped permissions limited to minimum necessary access.
  All sensitive personal information is encrypted, and access logs are maintained for auditing.

Performance, Scaling & Extensibility

As the volume of AI projects grew within the company, the automation demonstrated excellent scalability:

• Webhooks vs Polling: Webhooks from Jira allow near-real-time updates with minimal latency compared to polling, reducing resource usage and speeding reactions.
• Modular Workflows: The n8n workflows are modularized by function — triggers, approvals, data aggregation — enabling easy adaptation to new jurisdictions or teams.
• Queues and Parallelization: n8n supports concurrency, handling multiple change events simultaneously without backlogs.
• Extensibility: Adding integrations with third-party ERPs or CRMs is straightforward using custom HTTP Request nodes, allowing future-proof scaling.

Comparison Tables

Orchestration Tool	Cost	Pros	Cons
n8n	Free tier + affordable managed options	Open-source, flexible, strong API support, self-hostable	Requires more initial setup than plug-and-play tools
Make (Integromat)	Subscription-based, starts mid-range pricing	Rich UI, extensive app support, easy scenario editing	Less customizable, fewer self-host options
Zapier	Higher tiers costly for heavy usage	User-friendly, fast setup, strong app ecosystem	Limited logic complexity, no self-hosting

Integration Approach	Latency	Resource Use	Reliability
Webhooks (Event-driven)	Low – near real-time	Low – triggered on events only	High – reacts immediately
Polling (Scheduled checks)	Higher – depends on interval	Higher – frequent requests whether needed or not	Medium – delayed detection possible

Data Storage	Cost	Pros	Cons
Google Sheets	Free/tiered based on GSuite plan	Easy to set up, collaboration-friendly, compatible with many tools	Not suited for large data sets, potential concurrency issues
Cloud Database (SQL/NoSQL)	Variable, based on usage	Highly scalable, strong querying, concurrency safe	Higher setup complexity and cost

Results & Business Impact

The automation delivered measurable benefits shortly after deployment:

• Time Saved: Manual documentation hours dropped from 25 to less than 7 per month, a 72% reduction. [Source: client internal reporting]
• Error Reduction: Data consistency checks eliminated 90% of previous documentation discrepancies.
• Faster SLA Compliance: Audit-prep time shrank by 60%, enabling quicker regulator response.
• Improved Visibility: Real-time dashboards gave compliance teams full traceability and status updates at a glance.
• Calm Operations: Reduced team stress by automating approvals and timely alerts, ensuring human oversight remained effective without overload.

The engineering and compliance teams reported enhanced confidence in staying aligned with the EU AI Act, turning a formerly painful process into a smooth, transparent operation.

Pilot Phase & Maintenance Disclaimer

Following best practices, RestFlow implemented a robust pilot phase lasting six weeks where the automated workflows ran alongside manual processes. This stage allowed identification and resolution of minor bugs, adjustment of approval thresholds, and tuning for edge cases.

After pilot success, RestFlow provides ongoing managed hosting, monitoring, updates, and compliance audits to ensure the automation remains reliable and aligned with evolving regulatory requirements.

The pilot and continuous maintenance approach underscores that automation is a dynamic process refined over time with expert partnership.

Conclusion

The Milan engineering team’s success in building a regulator-ready evidence trail aligned with the EU AI Act demonstrates the power of automating the AI technical documentation pack. By partnering with RestFlow for Automation-as-a-Service, they turned a complex, manual burden into a streamlined, reliable, and scalable compliance solution.

This case study highlights the practical benefits of integrating tools like n8n, Jira, Google Sheets, and Slack within a well-designed workflow that supports risk management, transparency, and human oversight. Automation has directly accelerated audits, cut errors, and transformed compliance into a calm, manageable operation.

RestFlow remains committed to end-to-end service—designing workflows, implementing solutions, hosting and monitoring automation, and maintaining compliance readiness as regulations evolve.

Ready to transform your compliance process? Explore the Automation Template Marketplace or Create Your Free RestFlow Account today to start your automation journey.