This article provides a complete, practical walkthrough of the Code Manual Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Uncover Workplace Discrimination Patterns Using AI and Glassdoor Reviews
   
   Meta Description:  
   Learn how a no-code n8n workflow leverages OpenAI, ScrapingBee, and QuickChart to analyze Glassdoor reviews for signs of workplace bias across demographic groups—automatically spotting disparities and visualizing inclusion gaps.
   
   Keywords:  
   workplace bias analysis, Glassdoor scraper, DEI analytics, n8n automation, HR tech, OpenAI GPT, discrimination detection, QuickChart, ScrapingBee, workplace equity, demographic analysis, employee experience, inclusive workplace, bias detection tools, no-code AI workflows
   
   Third-Party APIs Used:
   
   1. ScrapingBee – For scraping Glassdoor search results, company pages, and demographic review data despite JavaScript rendering limitations.
   2. OpenAI – To extract structured demographic insights and review metrics using GPT-based large language models.
   3. QuickChart – For generating scatterplots and bar charts to visualize effect sizes, z-scores, and disparities among demographics using chart configuration objects.
   
   📘 Short Article:
   
   Spotting Workplace Discrimination with AI: A No-Code Glassdoor Analytics Workflow
   
   Workplace equity and inclusion have become central themes in modern HR practices. But for organizations looking to go beyond surface-level metrics, it’s often hard to quantify how equitable their environment really is—especially when it comes to the real lived experiences of employees from different demographic groups.
   
   This is where automation and AI can make a difference.
   
   Using an advanced n8n workflow powered by ScrapingBee, OpenAI, and QuickChart, anyone can now extract, analyze, and visualize bias indicators from publicly available Glassdoor reviews. The workflow spotlights discrepancies in satisfaction ratings across demographic groups, giving companies a data-driven foundation for discussions around workplace inclusion.
   
   Here’s how it works:
   
   Step 1: Scrape Glassdoor Reviews with ScrapingBee  
   The journey begins by querying Glassdoor using ScrapingBee, a headless browser API that bypasses the site's JavaScript-heavy structure. The tool retrieves the profile page and demographic review modules for a specified company like Twilio, enabling structured data extraction without manual browsing.
   
   Step 2: Extract Demographics and Ratings Using OpenAI  
   Once the raw HTML content is collected, the workflow deploys OpenAI’s GPT-based models to extract demographic-specific data from the reviews. This includes average ratings, total review counts, and percentile breakdowns. All the demographic groups—from racial identities to LGBTQIA+ status to veteran status—are parsed from structured fields set by the user earlier in the workflow.
   
   Step 3: Statistical Analysis Using Z-Scores and Effect Sizes  
   Once the data is structured, the workflow calculates metrics that are critical for detecting disparities:
   
   - Z-Score: How far a group’s experience rating deviates from the overall average, normalized by standard deviation.
   - Effect Size: The magnitude of difference between the group and overall experience—a higher negative value indicates a more considerable problem.
   - P-Score: Finally, it calculates p-values to check if the observed differences are statistically significant or likely due to random chance.
   
   These aren't just arbitrary stats—they provide concrete evidence supporting claims of systemic bias or inclusion gaps.
   
   Step 4: Visualization with QuickChart  
   With data calculated, the workflow formats it into beautiful, human-readable scatterplots and bar charts using QuickChart. Each point on the chart represents a demographic group with its z-score (x-axis) and effect size (y-axis), labeled clearly for easy interpretation. The visuals offer a bird’s-eye view of which communities feel most supported—or not—within the workplace.
   
   Step 5: AI-Powered Insight Summarization  
   Using yet another OpenAI node, the workflow concludes by summarizing key takeaways in plain language. For example, it might highlight that disabled or transgender employees have much lower workplace satisfaction, while veterans report above-average experiences. This makes it easier for HR teams and executives to take action.
   
   What Makes This Workflow Special?
   
   - Zero coding needed  
   Built entirely in n8n, this workflow can be run and modified with no programming experience. Users simply replace company names or swap out API credentials to rerun the analysis for other organizations.
   
   - Scalable and repeatable  
   Thanks to ScrapingBee’s generous free tier and the automation-friendly nature of n8n, the workflow can be reused across various scenarios—ideal for auditors, HR teams, or journalists.
   
   - Inspired by real advocacy  
   The blueprint draws inspiration from Wes Medford’s viral Glassdoor DEI analysis of Twilio. By automating the manual steps Medford took, this n8n workflow democratizes bias detection for less technical users.
   
   Toward Transparent, Fair Workplaces  
   Inclusion and equity aren’t just buzzwords—they’re measurable, improvable parts of workplace design. With this workflow, organizations and advocates alike gain a new toolset in their DEI arsenal. The ability to go from raw, unstructured reviews to statistically meaningful insight and visual representation makes this more than just automation—it’s ethical tech in action.
   
   Try it out, replace the company name, and start spotting the unseen patterns in your (or someone else’s) workplace.
   
   👋 Hi, Wes.
   
   —
   
   Tools Used:
   - n8n for orchestration
   - ScrapingBee for data scraping
   - OpenAI for AI reasoning and language processing
   - QuickChart for data visualization
   
   Ready to turn surveys, reviews, and anecdotes into data-backed advocacy? Automation has your back.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.