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:
   Uncovering Workplace Bias with AI: Automating Discrimination Detection Using n8n, Glassdoor, OpenAI & QuickChart
   
   Meta Description:
   Discover how to automate the detection of workplace discrimination through demographic-based review analysis using n8n workflows, data scraping from Glassdoor via ScrapingBee, AI extraction with OpenAI, and visualizations from QuickChart.
   
   Keywords:
   workplace discrimination analysis, glassdoor reviews, n8n automation, openai workflow, AI for HR, workplace bias detection, employee satisfaction, demographic analysis, DEI insights, quickchart.io, scrapingbee, z-score, effect size, p-value
   
   Third-Party APIs Used:
   1. ScrapingBee API – for extracting HTML content from Glassdoor review pages
   2. OpenAI API – for language model chat and information extraction from text
   3. QuickChart API – for generating scatterplots and bar charts from calculated insights
   4. Glassdoor – as the target data source for employee demographic reviews (data collected via scraping)
   
   Article:
   
   Spotting Patterns of Workplace Discrimination with AI—and Automation
   
   In a world increasingly focused on equity and inclusion, data-driven insights are proving crucial in identifying and addressing workplace disparities. A new no-code solution powered by the open-source tool n8n presents a compelling case study on how organizations—and concerned individuals—can automate the detection of workplace discrimination patterns using available public data and AI. By integrating scraping services, language models, and powerful visualizations, this workflow demonstrates how to bring equity analytics to the forefront.
   
   This walkthrough focuses on a use case analyzing Twilio’s employee reviews posted on Glassdoor—a particularly relevant company given recent whistleblower allegations shared in an open letter by Wes Medford. The workflow aims to scale that type of investigative analysis and make it accessible even for non-technical users.
   
   How It Works: The Workflow Breakdown
   
   At its heart, this n8n automation flows through a series of logical steps:
   
   1. Data Scraping from Glassdoor with ScrapingBee
   Due to the heavy use of JavaScript on Glassdoor pages, extracting structured data can be difficult with simple HTTP requests. Here, the ScrapingBee API plays a critical role, simulating full-fledged browsers to scrape company pages, review summaries, and demographic breakdowns. ScrapingBee collects:
   - The company’s primary Glassdoor review page
   - Detailed summaries of overall employee satisfaction
   - Employee reviews segmented by self-identified demographics
   
   2. Parsing and Extraction with OpenAI
   Once the HTML content is pulled, it’s passed through OpenAI’s GPT-based language models. These nodes perform intelligent parsing to extract structured information such as:
   - Average review scores by demographic group
   - Number of reviews per subgroup
   - Overall rating distributions from 1-star to 5-star
   
   This intelligent parsing is powered through OpenAI’s "information extractor" capabilities, enabling precise field-level data extraction without manual regex or scraping logic.
   
   3. Statistical Analysis: Z-Scores, Effect Sizes, and P-Scores
   With all relevant ratings extracted, the workflow calculates:
   - Z-Scores to measure how significantly each demographic deviates from the company’s mean rating
   - Effect Sizes to assess the magnitude of those deviations
   - P-Scores (P-values) to determine whether the differences are statistically significant
   
   These scores collectively allow for a quantifiable understanding of how different populations perceive their workplace—essentially measuring equity of experience.
   
   4. Visualizing the Disparity with QuickChart
   QuickChart.io is leveraged to generate highly readable charts:
   - A scatterplot mapping Z-Scores (x-axis) and Effect Sizes (y-axis), labeled by demographic
   - A bar chart ranking demographics by magnitude of effect size
   
   The result? A visually rich depiction of which groups are thriving—and which are suffering—inside the company.
   
   5. Executive Summary via AI Text Analysis
   Finally, the data is fed back into OpenAI, which produces plain-language executive summaries, including key takeaways and summaries of employee experiences. These insights help stakeholders better grasp the implications of the data, without requiring a technical background in statistics.
   
   Example Findings (Twilio Case)
   The specific test run on Twilio revealed the following:
   - Transgender and disabled employees reported among the lowest workplace satisfaction, with Z-Scores as low as -5.50 and large negative effect sizes.
   - LGBTQIA+ employees were also identified as experiencing significantly worse conditions than heterosexual peers.
   - Conversely, veterans reported above-average satisfaction, indicating favorable workplace experiences.
   
   These findings are plotted in visually digestible formats and tagged with labels for easy interpretation by HR, analytics teams, or executive leadership.
   
   Democratizing DEI Analytics
   
   This workflow does more than analyze employee sentiment—it democratizes access to advanced workplace equity analytics. By leveraging no-code automation and well-configured APIs, even small HR teams without data scientists can now run large-scale demographic performance audits.
   
   This n8n setup empowers users to:
   - Identify patterns of systemic discrimination
   - Validate whistleblower or Glassdoor claims with statistical rigor
   - Drive focused DEI interventions
   - Share AI-generated summaries with stakeholders
   
   Conclusion
   
   Equity is no longer a subjective goal—it’s a measurable, trackable metric. Through this workflow, n8n, OpenAI, ScrapingBee, and QuickChart combine into a holistic toolset for identifying and acting on discrimination in the workplace. This isn't just theoretical work—it's a blueprint for anyone seeking to promote fairness, with or without a technical background.
   
   As corporate transparency becomes more integral to public perception and talent retention, tools like this ensure accountability isn't just possible—it’s automated.

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.