AI-Assisted HR Recruitment Bot
Budget: ₹12,500 – ₹37,500 INR
Requirement Document
AI-Assisted Recruitment Bot
1. Project Title
AI-Assisted Recruitment and CV Screening Bot
2. Objective
The objective of this project is to develop a cost-effective AI-assisted recruitment system that automates the initial screening and management of candidate applications received through email.
The system should help reduce manual effort in sorting resumes, checking duplicate applications, storing candidate data, tracking interview progress, and shortlisting profiles based on predefined, dynamic job criteria.
3. Scope of Work
The freelancer/developer is expected to design and develop a lightweight recruitment assistance system with basic automation, AI-enabled screening, automated candidate communication, and full-lifecycle interview tracking workflows.
The system will be used occasionally (approximately 4-5 recruitment cycles per year); therefore, a low-maintenance and economical solution is preferred.
4. Functional Requirements
A. Email Monitoring & Domain Classification
· The system should monitor a designated Gmail account.
· It should identify incoming recruitment emails automatically.
· [New] Domain Extraction: As a first step, the bot must parse emails and classify applications based on subject/domain expertise and target level (e.g., Teaching, Non-Teaching, Administrative).
B. Resume/CV Extraction & Hyperlink Reporting
· The system should detect and extract resume attachments from emails.
· Supported formats may include PDF and DOC/DOCX.
· [New] Link Identification: The bot must identify and report external URLs within the CV or email body. It will extract and log:
1. LinkedIn Profile links
2. Personal webpage links
3. Portfolio links
4. Any other link, with a key AI-generated detail identifying the contents of the link.
C. Duplicate & Multi-Post Candidate Detection
· The bot should identify duplicate applications based on:
1. Email ID
2. Phone number
3. Candidate name
· Duplicate entries should be flagged or ignored.
· [New] Multi-Post Tracking: If the same candidate has applied for more than one advertised post, the bot must log the application without overwriting previous data, allowing recruiters to track the candidate across multiple distinct roles.
D. Candidate Data Storage (Expanded)
· Candidate details should be automatically stored in Google Sheets.
· [New] Format Flexibility: The system must provide provisions for exporting candidate data in formats other than Google Sheets (specifically Excel/.xlsx and CSV).
· Details stored will include:
1. Candidate Name
2. Email & Contact Number
3. [New] Academic Qualification (Highest)
4. Skills & Overall Experience (Segregated by Teaching, Non-Teaching, Admin)
5. [New] Expected Salary & Place of Residence (Distance)
6. Resume Link & Categorized Portfolio/LinkedIn Links
7. [New] Interview History, Demo Results, and Demo Report Links
E. AI-Based Screening & Granular Segregation
· AI should evaluate resumes against predefined job descriptions/criteria.
· The system should provide a matching score and basic recommendation.
· [New] Multi-Tier Screening Pool: The bot must segregate resumes based on residence, highest qualification, salary expectations, and overall experience. If the primary recruitment requirement is not met in Round 1, the bot must present a secondary list of candidates segregated into back-up screening pools.
F. Dashboard/Reporting & Dynamic Filtering
· A simple dashboard or report generation mechanism should be available.
· The system should allow viewing shortlisted candidates, exporting data, and tracking recruitment status.
· [New] Dynamic Range Sorting: The dashboard must feature a configuration page allowing recruiters to add custom fields and define specific ranges for selection, allowing data to be dynamically sorted by:
1. Salary between [Min] and [Max]
2. Distance between [Min] and [Max]
3. Past experience between [Min] and [Max]
G. Notifications & Automated Recruitment Workflow
· Stage 1: Automated acknowledgment email sent to candidates.
· [New] Stage 2 (Form Collection): An automated email must be sent to shortlisted candidates who meet initial criteria, prompting them to fill out a detailed candidate form.
· [New] Stage 3 (Interview Line-up): Provision for scheduling interviews. The bot will send an interview scheduling email to the candidate and track their response (Accept/Decline).
· [New] Stage 4 (Recruiter Alerts & Updates): Automated notifications to the recruiter to update the "Recruitment Status". The recruiter can update the status through milestones: Demo Recommended, Rejected, Demo Report Link Updated, or Selected.
H. [New] Multi-Cycle Historical Validation
· When a new cycle of recruitment begins, the bot must first validate all incoming applications against the database's historical records.
· It must automatically separate previously non-shortlisted, rejected, and non-reported (no-show) resumes from fresh profiles.
5. Technical Requirements
Preferred technologies/tools:
· Google Apps Script
· Gemini API/OpenAI API (if required)
· Gmail Integration
· Google Sheets
· Cloud-based low-cost architecture
The solution should be:
· Easy to maintain, user-friendly, and scalable for moderate future usage.
However, open to any cost effective solution.
6. Non-Functional Requirements
· [New] Data Privacy Compliance: Secure handling of candidate data and reliable data storage strictly in accordance with the Digital Personal Data Protection (DPDPA) Act.
· Minimal manual intervention
· Low operational cost
· Simple user interface
7. Expected Deliverables
The freelancer/developer should provide:
1. Working recruitment bot/system (including the dynamic range selection page)
2. Source code/scripts
3. Deployment/setup support
4. User manual/basic documentation (including historical validation & data purging steps for DPDPA compliance)
5. Testing and demonstration
8. Estimated Usage
· Expected usage frequency: 4-5 times annually
· Candidate volume:
o With Advertisement: 150+ CV’s weekly.
o Without Advertisement: 50-60 CV’s weekly.
9. Budget Consideration
A cost-effective solution is preferred.
10. Timeline
Expected development and deployment timeline:
· Approximately 3–6 weeks from project approval.
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AI-Assisted Recruitment Bot
1. Project Title
AI-Assisted Recruitment and CV Screening Bot
2. Objective
The objective of this project is to develop a cost-effective AI-assisted recruitment system that automates the initial screening and management of candidate applications received through email.
The system should help reduce manual effort in sorting resumes, checking duplicate applications, storing candidate data, tracking interview progress, and shortlisting profiles based on predefined, dynamic job criteria.
3. Scope of Work
The freelancer/developer is expected to design and develop a lightweight recruitment assistance system with basic automation, AI-enabled screening, automated candidate communication, and full-lifecycle interview tracking workflows.
The system will be used occasionally (approximately 4-5 recruitment cycles per year); therefore, a low-maintenance and economical solution is preferred.
4. Functional Requirements
A. Email Monitoring & Domain Classification
· The system should monitor a designated Gmail account.
· It should identify incoming recruitment emails automatically.
· [New] Domain Extraction: As a first step, the bot must parse emails and classify applications based on subject/domain expertise and target level (e.g., Teaching, Non-Teaching, Administrative).
B. Resume/CV Extraction & Hyperlink Reporting
· The system should detect and extract resume attachments from emails.
· Supported formats may include PDF and DOC/DOCX.
· [New] Link Identification: The bot must identify and report external URLs within the CV or email body. It will extract and log:
1. LinkedIn Profile links
2. Personal webpage links
3. Portfolio links
4. Any other link, with a key AI-generated detail identifying the contents of the link.
C. Duplicate & Multi-Post Candidate Detection
· The bot should identify duplicate applications based on:
1. Email ID
2. Phone number
3. Candidate name
· Duplicate entries should be flagged or ignored.
· [New] Multi-Post Tracking: If the same candidate has applied for more than one advertised post, the bot must log the application without overwriting previous data, allowing recruiters to track the candidate across multiple distinct roles.
D. Candidate Data Storage (Expanded)
· Candidate details should be automatically stored in Google Sheets.
· [New] Format Flexibility: The system must provide provisions for exporting candidate data in formats other than Google Sheets (specifically Excel/.xlsx and CSV).
· Details stored will include:
1. Candidate Name
2. Email & Contact Number
3. [New] Academic Qualification (Highest)
4. Skills & Overall Experience (Segregated by Teaching, Non-Teaching, Admin)
5. [New] Expected Salary & Place of Residence (Distance)
6. Resume Link & Categorized Portfolio/LinkedIn Links
7. [New] Interview History, Demo Results, and Demo Report Links
E. AI-Based Screening & Granular Segregation
· AI should evaluate resumes against predefined job descriptions/criteria.
· The system should provide a matching score and basic recommendation.
· [New] Multi-Tier Screening Pool: The bot must segregate resumes based on residence, highest qualification, salary expectations, and overall experience. If the primary recruitment requirement is not met in Round 1, the bot must present a secondary list of candidates segregated into back-up screening pools.
F. Dashboard/Reporting & Dynamic Filtering
· A simple dashboard or report generation mechanism should be available.
· The system should allow viewing shortlisted candidates, exporting data, and tracking recruitment status.
· [New] Dynamic Range Sorting: The dashboard must feature a configuration page allowing recruiters to add custom fields and define specific ranges for selection, allowing data to be dynamically sorted by:
1. Salary between [Min] and [Max]
2. Distance between [Min] and [Max]
3. Past experience between [Min] and [Max]
G. Notifications & Automated Recruitment Workflow
· Stage 1: Automated acknowledgment email sent to candidates.
· [New] Stage 2 (Form Collection): An automated email must be sent to shortlisted candidates who meet initial criteria, prompting them to fill out a detailed candidate form.
· [New] Stage 3 (Interview Line-up): Provision for scheduling interviews. The bot will send an interview scheduling email to the candidate and track their response (Accept/Decline).
· [New] Stage 4 (Recruiter Alerts & Updates): Automated notifications to the recruiter to update the "Recruitment Status". The recruiter can update the status through milestones: Demo Recommended, Rejected, Demo Report Link Updated, or Selected.
H. [New] Multi-Cycle Historical Validation
· When a new cycle of recruitment begins, the bot must first validate all incoming applications against the database's historical records.
· It must automatically separate previously non-shortlisted, rejected, and non-reported (no-show) resumes from fresh profiles.
5. Technical Requirements
Preferred technologies/tools:
· Google Apps Script
· Gemini API/OpenAI API (if required)
· Gmail Integration
· Google Sheets
· Cloud-based low-cost architecture
The solution should be:
· Easy to maintain, user-friendly, and scalable for moderate future usage.
However, open to any cost effective solution.
6. Non-Functional Requirements
· [New] Data Privacy Compliance: Secure handling of candidate data and reliable data storage strictly in accordance with the Digital Personal Data Protection (DPDPA) Act.
· Minimal manual intervention
· Low operational cost
· Simple user interface
7. Expected Deliverables
The freelancer/developer should provide:
1. Working recruitment bot/system (including the dynamic range selection page)
2. Source code/scripts
3. Deployment/setup support
4. User manual/basic documentation (including historical validation & data purging steps for DPDPA compliance)
5. Testing and demonstration
8. Estimated Usage
· Expected usage frequency: 4-5 times annually
· Candidate volume:
o With Advertisement: 150+ CV’s weekly.
o Without Advertisement: 50-60 CV’s weekly.
9. Budget Consideration
A cost-effective solution is preferred.
10. Timeline
Expected development and deployment timeline:
· Approximately 3–6 weeks from project approval.
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