About the Agent
Overview
Challenges
Missed Opportunities:
Sales teams often experienced delays in following up on leads captured through HubSpot forms due to lack of instant alerts.
Manual Notifications:
Sending lead details manually across channels was error-prone, inconsistent, and inefficient.
Distributed Teams:
Regional sales teams lacked visibility into real-time lead capture events without directly accessing HubSpot.
Integration Complexity:
Existing notification solutions were either inflexible or required extensive development effort to maintain.
Challenges
Time-Consuming Resume Review
Manually reading and shortlisting resumes slowed down the recruitment cycle.
Inconsistent Evaluation Criteria
Candidate assessments varied between HR team members.
Generic Interview Questions
Lack of personalization led to irrelevant and unstructured interviews.
Delayed Communication
HR took days to get back to candidates due to high application volume.
No Smart Prioritization
High-potential candidates were not being flagged early.
Objectives / Goals
- Automate resume intake, parsing, and evaluation.
- Score applicants based on skills, experience, and fit with job descriptions.
- Generate structured and relevant interview questions using AI.
- Deliver formatted reports to HR with minimal manual effort.
- Prioritize high-fit candidates automatically.
Objectives
This automation aimed to:
- Deliver real-time alerts to Teams when a new lead is created in HubSpot
- Provide clear, structured lead information (name, email, phone, title, status)
- Support custom routing logic for different Teams groups or regional channels
- Minimize reliance on sales reps checking HubSpot directly
- Allow easy extensibility for CRM syncing and AI-based lead scoring
Solution Architecture
Candidate Intake & Data Capture
AI-Powered Decision Engine
- A Google Form is used to collect candidate information:
- Name, Email, Phone Number, Applied Position, Resume (uploaded via Google Drive)
- Responses are stored automatically in Google Sheets (Sheet 1).
Role-Specific Knowledge Base Setup
- Google Sheets (Sheet 2) acts as a dynamic job description repository:
- Each row contains a job title and its corresponding job description.
- The candidate’s selected position is used to lookup the correct job description.
Resume Retrieval & Parsing
- The system extracts the Google Drive File ID from the resume link submitted via the form.
- Using n8n’s Google Drive and PDF extraction nodes, the workflow:
- Downloads the resume file.
- Extracts and cleans the resume content for evaluation.
Resume Retrieval & Parsing
- The extracted resume and matched job description are sent to OpenAI GPT.
- OpenAI compares the candidate’s:
- Skills, education, certifications, experience, tools/technologies.
- Returns:
-
📊 Compatibility Score (out of 100).
✅ Hiring Recommendation (e.g., Strong Fit, Moderate Fit, Not Recommended).
Interview Question Generation (OpenAI Node 2)
- Based on the resume and job description, OpenAI generates:
- Customized, role-aligned interview questions.
- Customized, role-aligned interview questions.
- Structured in a clear, grouped format for ease of use during interviews.
Automated HR Notification
- All results are compiled into a professionally formatted email via the Gmail Node:
- Candidate
Key Technologies Used:
- n8n Workflow Automation
- Google Forms & Sheets (as input source & knowledge base)
- Google Drive (for resume uploads)
- OpenAI GPT (for scoring and question generation)
- Gmail API (for HR notifications)
- PDF Extraction Nodes
- Custom Logic Handling using Code Nodes
Duration & Resources:
- Time taken: 4–5 Days
- Resources: 1 Automation Developer
Use Cases:
- Auto-Screening New Applicants: Every application submitted via Google Form is automatically evaluated and scored.
- Role-Specific Evaluation Resume is matched with role-specific job description for high-accuracy scoring.
- Personalized Interview Questions: Skill-based, categorized questions generated dynamically for each candidate.
- Automated HR Alerts: Screening reports (score, questions, recommendation) sent instantly to HR inbox.
- Candidate Prioritization: Gmail auto-labeling helps HR easily identify top-scoring candidates for next steps.
Outcomes
| Metric | Before Automation | After Automation | Improvement |
|---|---|---|---|
| Initial Screening Time per Candidate | 25–30 minutes manually | 1–2 minutes (automated processing) | ⬇️ 90% faster |
| Shortlisting Accuracy (Job Fit Relevance) | Subjective & inconsistent | Standardized score-based evaluation | ⬆️ 60% more accurate |
| Time to Deliver HR Reports | 12–24 hours per batch | < 10 minutes from submission | ⬇️ 95% time reduction |
| Candidate Engagement Turnaround | Delayed due to manual backlog | Prompt follow-up possible within same day | ⬆️ 80% faster response |
| HR Bandwidth Utilized in Screening | ~70% of HR effort spent on resume review | < 20%, HR only reviews shortlisted outputs | ⬇️ 50% saved effort |
| Consistency in Interview Question Quality | Varied, manually copied from templates | AI-generated, role-specific, skill-targeted | ⬆️ 90% improvement |
| Email Labeling for High-Priority Candidates | Manual tagging | Automated Gmail labeling | ✅ Fully automated |
Conclusion
The Automated Interview Screening Agent significantly streamlined the candidate evaluation process by integrating AI-driven analysis, smart document parsing, and real-time email reporting. This solution not only accelerated decision-making but also enhanced consistency and objectivity in early-stage hiring.
It serves as a scalable, intelligent foundation for future-ready recruitment operations, empowering HR teams to focus on quality engagement over repetitive tasks.