This article provides a complete, practical walkthrough of the Manual Wait 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:  
   Automating Company Insights: Using n8n, Google Gemini, and Bright Data to Summarize Glassdoor Company Profiles
   
   Meta Description:  
   Discover how to automate the collection and summarization of Glassdoor company data by integrating Bright Data’s scraper API with Google Gemini’s LLM through an n8n workflow for rapid HR insights.
   
   Keywords:  
   n8n, Bright Data, Google Gemini, Glassdoor API, HR automation, AI summarization, data scraping, LLM, workflow automation, Summarization Chain, LangChain, employee reviews, company insights
   
   Third-Party APIs Used:
   
   1. Bright Data Dataset API – for scraping real-time data from Glassdoor
   2. Google Gemini (PaLM) API – for AI-powered summarization via LLM
   3. webhook.site – for delivering the final summary via Webhook URL
   
   Article:
   
   Unlocking Company Insights with n8n, Google Gemini, and Bright Data Web Scraper
   
   In the digital age, company research plays a crucial role in HR strategy, recruitment, and competitive benchmarking. Glassdoor, a leading platform for employer reviews and company ratings, is often the go-to place for gauging company reputation and employee satisfaction. But manually gathering and analyzing this information can be tedious and error-prone. This is where automation steps in to bridge the gap between raw data and actionable insights.
   
   Combining the robust automation capabilities of n8n with Bright Data's scraping tools and Google Gemini’s advanced AI models, this powerful workflow simplifies the entire process—from data extraction to AI summarization—making HR and competitive analysis faster, smarter, and more efficient.
   
   Let’s break down how this intelligent workflow functions.
   
   🧠 The Purpose of This Workflow
   
   The goal is to automate the summarization of a Glassdoor company profile. Specifically, it collects a company’s profile (e.g., for Apple) using Bright Data’s dataset scraper, waits for the data to be fully retrieved, and then passes the content through a Large Language Model (LLM)—in this case, Google Gemini—to generate a human-readable summary. Finally, the summary can be exported, shown in a dashboard, or sent directly to a Webhook endpoint.
   
   ⚙️ Step-by-Step Overview of the Workflow
   
   1. Triggering the Workflow
   The workflow begins with a manual trigger in n8n, aptly named “When clicking ‘Test workflow’.” This gives the user control over when to run the data collection cycle—ideal for testing or use in scheduled automation.
   
   2. Collecting Glassdoor Data with Bright Data
   The HTTP request node sends a POST request to Bright Data’s Dataset API. It deploys a pre-configured scraper to capture data from a Glassdoor company page—in this example, Apple’s UK profile. The request includes parameters such as dataset ID and whether to return errors.
   
   The response from this step includes a snapshot_id which uniquely identifies the scraping operation that just began.
   
   3. Tracking Scrape Progress
   The “Set Snapshot Id” node extracts and retains the snapshot ID for subsequent steps. The “Check Snapshot Status” node periodically pings Bright Data to check if the dataset is ready using the snapshot ID in the API call.
   
   If the dataset status is marked as “ready”, the workflow proceeds; otherwise, it waits for 30 seconds and checks again. This retry logic ensures that the process is asynchronous and resilient.
   
   4. Downloading and Preparing the Data
   Once the data snapshot is ready, another HTTP node fetches the actual Glassdoor company data in JSON format.
   
   The text is then split into smaller, manageable chunks by the “Recursive Character Text Splitter.” This is essential for feeding structured input into an LLM, which typically performs better with length-constrained input.
   
   5. Summarizing with Google Gemini AI
   The document chunks are loaded and passed into the “Summarization Chain” node, powered by LangChain, which uses Google Gemini (PaLM) as the LLM.
   
   This Google model is defined as “models/gemini-2.0-flash-thinking-exp-01-21”, an experimental model designed for fast inference and summarization. Gemini processes the structured pieces of company data and returns a readable, concise summary that highlights key company insights, employee feedback, and workplace culture.
   
   6. Delivering the Final Summary
   Once the summary is complete, it’s sent via a Webhook HTTP request to a predefined destination—in this case, webhook.site. This flexible endpoint allows integration into email platforms, dashboards, or applicant tracking systems (ATS).
   
   📌 Why This Matters for HR and Business Intelligence
   
   - Speed: Automating the collection and summarization of company profiles allows HR professionals and recruiters to get quick snapshots of company reputations.
   - Consistency: Google Gemini provides consistent summarization, reducing bias and interpretation errors across datasets.
   - Integration: n8n’s modular nature means this workflow can be extended to cover multiple companies, automated schedules, or even integrate into BI and CRM platforms.
   - Customization: With full access to each API parameter, organizations can tailor the data they scrape and how it’s processed and presented.
   
   🔍 Use Case Examples
   
   - Competitive Analysis: Enterprises monitoring peers or recruitment competitors can automate the regular retrieval of reputational insights.
   - Recruitment Strategy: Agencies or departments evaluating a company before engaging candidates can ingest summaries directly into their ATS.
   - Due Diligence: Investors and M&A teams can collect reputational data to assess company culture risks before deals.
   
   🧩 Modular and Scalable
   
   While this workflow currently targets Glassdoor data on a single company, it’s simple to scale it for multiple URLs or perform sentiment analysis by enriching the Gemini model prompts.
   
   Likewise, webhook delivery could be replaced with enriched reporting via Slack, Google Sheets, Notion, or Airtable—making this a versatile foundation for business intelligence operations.
   
   📦 Conclusion
   
   This n8n workflow showcases the combined power of web scraping, automation, and generative AI. By leveraging Bright Data and Google Gemini in tandem with n8n, teams can monitor, analyze, and summarize corporate reputation data with minimal manual intervention.
   
   Whether you’re in human resources, competitive research, or data science, this integration offers a reliable and intelligent method to transform unstructured data into clear, actionable insights.
   
   With tools like these, the future of work is not just automated—it’s augmented.
   
   —  
   Written by your friendly AI workflow analyst 🤖

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.