This article provides a complete, practical walkthrough of the Form Extractfromfile Automate Triggered 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:  
   Streamlining Recruitment with AI: A Deep Dive into the n8n Automated CV Screening Workflow
   
   Meta Description:  
   Discover how the "AI CV Screening Workflow" built with n8n automates applicant tracking, processes resumes with Google Gemini AI, and notifies HR teams instantly, boosting hiring efficiency.
   
   Keywords:  
   n8n automation, AI CV screening, resume analysis, Google Gemini AI, recruitment automation, workflow automation, HR tech, candidate evaluation, Gmail API, Google Sheets API, PDF to JSON, form submission automation
   
   Third-Party APIs Used:  
   
   1. Google Gemini (PaLM) API — For AI-assisted resume analysis and ranking  
   2. Gmail API (via Gmail OAuth2) — For sending emails to HR and candidates  
   3. Google Sheets API (via Google Sheets OAuth2) — For storing structured candidate data  
   4. LangChain (Google Gemini node via LangChain) — For AI interactions in natural language  
   5. n8n Form Trigger Node — For collecting application and resume data  
   6. ExtractFromFile Node — For parsing resume files (PDF to JSON content)
   
   ---
   
   Article:
   
   In the fast-paced world of talent acquisition, human resource teams are constantly striving to streamline the hiring process while ensuring high-quality applicant screening. The manual review of resumes not only consumes time and effort but can also introduce inconsistencies. Enter n8n’s “AI CV Screening Workflow” — an intelligent, automated pipeline that enhances recruitment efficiency by combining workflow automation with advanced AI capabilities.
   
   This article breaks down how the workflow works, the benefits it offers, and how it leverages third-party APIs to simplify and supercharge the hiring process.
   
   🙋‍♂️ Step 1: Application Submission via Custom Form
   
   The workflow begins with the “Application Form” node. Built using n8n’s Form Trigger, it captures essential candidate information for a Software Engineer role. Applicants are required to provide:
   
   - Full Name  
   - E-mail Address  
   - Salary Expectation  
   - LinkedIn Profile  
   - Resume (PDF format)
   
   This ensures all critical data points are standardized and gathered upfront, avoiding follow-up tasks and data wrangling down the line.
   
   🔍 Step 2: Resume Conversion and Analysis
   
   Once a resume file is uploaded, the “Convert Binary to JSON” node kicks in. It extracts content from the uploaded PDF resume, transforming it into a JSON format that AI can interpret.
   
   Next, the AI analysis engine, powered by the “Using AI Analysis & Rating” node, takes center stage. Powered by Google Gemini via LangChain integration, this AI evaluates the candidate’s resume against a predefined job description (in this case, “Software Engineer”). What makes this special isn’t just the use of AI — it’s how detailed and structured the prompt is:
   
   - A compatibility rating from 1 to 10  
   - A rationale explaining the rating  
   - A final recommendation on whether to consider the candidate for an interview  
   
   This level of insight goes beyond keyword matching. The AI mimics professional HR filter logic, helping employers see beyond the paper qualifications and into the likely fit of the candidate.
   
   📥 Step 3: Seamless HR Notification and Candidate Follow-Up
   
   Depending on the AI-generated assessment, the process branches into three next steps:
   
   1. Storing Candidate Info:  
   The analyzed data is neatly logged into a pre-configured Google Sheet named “CV of Software Engineers.” Key attributes such as CV filename, email, LinkedIn, salary expectation, AI rating, and full name are saved automatically.
   
   2. HR Notification:  
   The “Inform HR New CV Received” node uses Gmail API to notify the HR email address. This message includes all submitted information and the AI rating, enabling the HR team to quickly determine follow-up actions.
   
   3. Candidate Acknowledgment:  
   Simultaneously, the candidate receives a confirmation email sent automatically via another Gmail API call. This closes the loop politely and informs them that their submission has been received and is under review.
   
   🤖 Step 4: Putting the “Smart” in Smart Automation
   
   This automation not only reduces manual labor but introduces high-quality, consistent evaluations into the early hiring funnel. With the use of Google Gemini AI and Google Sheets, all candidate details are both consultable and analyzable — enabling sourcing teams to slice and dice applicant data whichever way they need.
   
   In a context where AI is often feared as a job replacement technology, workflows like this showcase the real power of AI: collaboration. It augments HR professionals' abilities and frees up time for higher-value tasks like interviews, team assessments, and cultural fit evaluations.
   
   ---
   
   Wrapping Up
   
   The “AI CV Screening Workflow” is a powerful demonstration of how low-code automation paired with AI can modernize and future-proof traditional business processes like recruitment. From intelligently parsing resumes to organizing and evaluating candidates, and from notifying HR to acknowledging candidates — the entire hiring pipeline becomes faster, smarter, and more structured.
   
   For companies struggling with high applicant volume or limited HR bandwidth, this type of workflow offers both a competitive edge and operational relief.
   
   With tools like n8n and Google Gemini in your stack, your recruitment funnel can work smarter — not harder.
   
   —  
   Written by your AI Assistant  
   Always Here to Automate, Assist, and Augment

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.