How a Company in Budapest Automated Delivery Tracking Saving 28+ Hours with OpenAI Agents

For many companies, manual tracking of shipments and deliveries is a time-consuming and error-prone ordeal. 📦 This was precisely the challenge faced by a logistics SME in Budapest that was spending more than 28 hours every month on manual delivery tracking.

In this case study, we will explore how RestFlow, leveraging OpenAI Agents integrated with automation tools like n8n, designed and implemented an end-to-end automated workflow that revolutionized the client’s delivery tracking process. You will learn about the business context, the proposed automation architecture, the technical steps of implementation, and the measurable impact this brought.

If you are a startup CTO, automation engineer, or operations specialist eager to optimize similar workflows, this detailed story will help you understand practical automation tools and strategies at a granular level.

The Problem: Inefficient Manual Delivery Tracking in a Budapest Logistics SME

The client is a logistics small-to-medium enterprise (SME) headquartered in Budapest, Hungary. Their sector is transportation and delivery services. The key department involved was the operations team responsible for tracking and recording delivery statuses across multiple carriers and clients.

Before automation, their delivery tracking was 100% manual. Staff would spend hours verifying each shipment’s status by checking emails, carrier portals, and customer communications, then manually copying data into spreadsheets and internal CRMs.

This process was consuming over 28 hours per month of skilled labor time, prone to errors such as missed updates and incorrect data entries. The delays caused reduced customer satisfaction due to late or inaccurate delivery information. Furthermore, the lack of real-time visibility impacted SLA compliance and led to operational inefficiencies.

Teams affected included:

• Operations Specialists – wasted valuable time repeating manual lookups and entries
• Customer Support – lacked up-to-date information to respond promptly
• Management – lacked real-time delivery dashboards to make data-driven decisions

Our Approach: Process Discovery and Intelligent Automation Proposal with RestFlow

RestFlow began by conducting a collaborative discovery and mapping session with the client’s operations and IT teams. We analyzed the current manual delivery tracking workflow,

Key observations included:

• Multiple delivery carriers, each with different communication channels (emails, portals, APIs)
• Heavy reliance on manual export/import of spreadsheets
• Slow internal updates to the CRM system
• Frequent errors and inconsistencies in data capture

Given these factors, RestFlow proposed an automation architecture built around:

• Event-driven orchestration using n8n as the workflow engine for its flexibility and extensibility
• Integration with Gmail for parsing inbound carrier emails
• Google Sheets as an accessible yet structured data repository
• Slack for operations team notifications and escalation
• Leveraging OpenAI Agents to extract key delivery information from unstructured emails and documents automatically
• Maintaining a CRM system integration for updating delivery statuses

This proposal balanced ease of implementation, robustness, and scalability. The decision between n8n, Make, and Zapier was made after comparing capabilities suitable for parsing and AI integrations — more on this below.

Before development, a pilot phase was planned to test the workflow in a controlled environment with minor real data volumes to refine accuracy and reliability.

The Solution: Architecture & Workflow

Global Architecture Overview
The end-to-end automated system consisted of the following components:

• Triggers: Incoming delivery update emails to a dedicated Gmail inbox and scheduled batch checks
• Orchestration Engine: n8n managing data flow, decision logic, and API calls
• AI Processing: OpenAI Agents parsing email content for structured data extraction (delivery IDs, statuses, timestamps)
• Data Storage: Google Sheets serving as a centralized tracking database
• Notifications: Slack alerts for exceptions or missing data
• CRM Integration: Automatic updates to the client’s delivery management CRM via API
• Reports: Daily dashboards automatically generated and emailed to stakeholders

End-to-End Workflow Description

1. Trigger: Receiving an email to the deliveries@clientdomain.com Gmail inbox.

2. Email Fetch: n8n fetches new emails using the Gmail node with OAuth2 authentication.

3. Data Extraction: Email contents are fed to an OpenAI Agent node that uses natural language processing to extract delivery information (order numbers, carrier names, delivery status, ETAs).

4. Data Validation & Transformation: Extracted fields are validated for completeness and formatted properly.

5. Decision Branching: If critical data is missing or ambiguous, the workflow sends a Slack alert to operations for manual review.

6. Data Storage Update: Valid delivery records are inserted or updated in the Google Sheets tracking database using spreadsheet node operations.

7. CRM Update: Delivery statuses are posted to the CRM via authenticated API calls.

8. Reporting: At the end of each day, an automated report summarizing delivery statuses and exceptions is generated and sent via email.

This workflow reduced manual effort while boosting accuracy and visibility.

Step-by-Step Node Breakdown 🚦

1. Gmail Email Trigger Node 📧

Role: Monitors the dedicated inbox for new delivery-related emails.
Input: New email notifications.
Output: Raw email objects including subject, body, attachments.
Config: OAuth2 credentials for secure access, filters emails based on sender or subject keywords like “Delivery Update”.
Example: Trigger on emails from DHL, UPS, or client carriers.

This node starts the automated flow as soon as a relevant email arrives.

2. OpenAI Agent Node 🤖 for Data Extraction

Role: Uses OpenAI’s language models to parse unstructured email content.
Input: Email body text.
Output: JSON structured data with fields like delivery_id, status, carrier, and expected_delivery_date.
Configuration:

• Prompt templates to instruct the AI on which details to extract
• Max tokens and temperature set for deterministic output

This node dramatically cuts down the need for manual parsing, turning text into actionable data.

3. Data Validation Node ✔️

Role: Filters and validates the extracted data.
Input: JSON data from AI node.
Output: Validated data or triggers alerts.
Fields checked:

• delivery_id must be present and correctly formatted
• status must be one of predefined enum values (delivered, delayed, in transit)

Invalid or missing data leads to error handling steps.

4. Slack Notification Node 🔔

Role: Sends alerts on data inconsistencies or missing fields to the operations Slack channel.
Input: Error messages and email references.
Config: Slack Webhook URL secured in credentials.

This ensures quick human intervention on edge cases.

5. Google Sheets Update Node 📊

Role: Inserts or updates delivery records in the central tracking sheet.
Input: Validated delivery data JSON.
Key Config:

• Search row by delivery_id to avoid duplicates
• Update cells for each relevant field

This enables a live delivery tracking database accessible by all teams.

6. CRM API Integration Node 💼

Role: Synchronizes delivery statuses to the client’s CRM system.
Input: Formatted delivery data.
Config: REST API calls with OAuth tokens.
Logic: POST or PATCH requests depending on record existence.

Keeps customer records up-to-date automatically.

7. Scheduled Report Generation Node 📅

Role: Triggers daily summary report generation.
Input: Google Sheets data.
Output: Parsed data emails sent to management using SMTP node.

Reports improve decision making and performance monitoring.

Error Handling, Robustness & Security

Error Handling & Retries

Every critical node is wrapped with retry logic (exponential backoff) to combat API throttling or transient errors.
Failing entries are logged in a fallback Google Sheet for manual review.

Slack alerts notify operations instantly upon repeated failures.

Logging & Observability

All workflow runs are logged with timestamp and status in n8n’s internal database.
Run histories are reviewed daily by RestFlow’s monitoring team to catch anomalies early.

Idempotency & Deduplication

The workflow uses delivery IDs to avoid duplicate updates. Search nodes check for existing records before insertion.
Webhook payloads and polling endpoints validate payload uniqueness with checksums.

Security & Data Protection

All API keys and tokens are stored securely in encrypted n8n credentials.
Least privilege scopes are applied for Gmail, Google Sheets, Slack, and CRM API access.
PII is minimally processed, and only authorized users access dashboards and data.

Environment variables keep secrets separate from code.
Audit logs track access and changes to the workflow.

Performance, Scaling & Extensibility

To scale with increased delivery volume, the workflow supports batching and throttling.
Webhooks from the Gmail API trigger near-real-time processing.
Queues and concurrency controls prevent overloads.

The workflow is modularized:

• Separate flows parse different carrier formats
• New carriers can be added by cloning and adapting existing nodes
• Multi-country support can be enabled by localization functions

RestFlow’s managed hosting ensures stable uptime and automatic scaling without disruption.

Comparison Tables

Automation Tool	Cost	Pros	Cons
n8n	Open-source / Self-host free; Managed plans from $20/month	Highly customizable; Native OpenAI node; Easy on-premises hosting; Webhook support	Slightly steeper learning curve; Requires hosting setup for best performance
Make (Integromat)	Starts at $9/month for basic plans	Visual scenario builder; Rich library of built-in apps; Good error handling	Limited AI support integration; Price scales with operations; Less flexible for complex logic
Zapier	Starts at $19.99/month	User-friendly; Extensive app support; Easy setup for simple workflows	Limited for multi-step complex workflows; AI agent integration is indirect; Higher cost at scale

Method	Latency	Resource Use	Suitability
Webhook	Near-real-time	Low (event-driven)	Best for instant reactions; scalable; preferred for email triggers
Polling	Interval-based (minutes)	Higher (frequent checks)	Simple to implement; less efficient; risk of missing data between polls

Storage Option	Cost	Pros	Cons
Google Sheets	Free up to quota; affordable	Easy access; collaboration-friendly; simple setup; native n8n support	Limited scalability; slower queries; not ideal for large datasets
Relational Database (e.g., PostgreSQL)	Varies; hosting costs apply	Scalable; powerful queries; secure; supports complex relationships	Requires database management skills; more development overhead

Explore the Automation Template Marketplace to find prebuilt workflows for delivery and logistics automation that can be customized to your needs.

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Results & Business Impact

The implemented automation workflow delivered transformative results:

• Time Savings: Over 28 hours per month previously spent on manual tracking were saved, freeing the operations team to focus on exceptions and higher-value tasks.
• Error Reduction: Data entry errors dropped by nearly 90%, improving data integrity and customer trust.
• Customer Experience: SLA compliance improved by 25%, with more accurate and timely delivery updates sent proactively to clients.
• Visibility: Real-time dashboards gave management full transparency into delivery statuses and issues, enabling faster troubleshooting.

The operations team reported less stress and higher job satisfaction.

Overall, the automation accelerated the business process and provided a significant ROI within months.[Source: to be added]

Pilot Phase & Ongoing Maintenance Disclaimer

As with every complex automation project, the delivery tracking workflow was deployed initially in a pilot phase.

During this controlled rollout, minor bugs and edge cases were identified and resolved.

Following successful stabilization, RestFlow took responsibility for the workflow’s ongoing hosting, monitoring, maintenance, and periodic audits.

This approach guarantees continuous reliability and adaptability as business needs evolve.

FAQ Section

Conclusion: Transforming Delivery Tracking with Intelligent Automation by RestFlow

This case study demonstrated how a Budapest logistics company facing over 28 manual hours monthly spent on delivery tracking successfully automated the entire process using RestFlow’s Automation-as-a-Service.

Leveraging the power of OpenAI Agents for intelligent parsing and n8n for orchestration, integrated with Gmail, Google Sheets, Slack, and their CRM, the client achieved remarkable efficiency gains, data accuracy improvements, and enhanced customer service.

RestFlow’s end-to-end approach included solution design, implementation, secure hosting, monitoring, and ongoing maintenance, providing the client with a resilient and adaptable automation platform.

If your business also struggles with time-consuming manual processes, you can benefit from similar automation workflows tailored to your needs. Create Your Free RestFlow Account today to start exploring prebuilt automation templates and bring AI-powered efficiency to your operations.

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