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Recruitment & applicant tracking system

HireLoop — Recruitment & applicant tracking system Blueprint

HireLoop is a modern, AI-powered applicant tracking system designed to simplify and accelerate the hiring process for growing companies.

The opportunity

Traditional ATS platforms are often clunky, outdated, and lack modern features like AI-driven candidate matching and collaborative workflows. This leads to inefficient hiring, lost talent, and a poor candidate experience, especially for fast-paced companies.

Who it's for

Key features

Business model

Recommended architecture

Recommended tech stack

Frontend

React with Next.js; Provides excellent developer experience, server-side rendering for performance, and a robust ecosystem.

Backend

Node.js (TypeScript) with Express.js; Fast development, single language for full-stack, and strong asynchronous capabilities for I/O heavy tasks.

Database

PostgreSQL; Robust, ACID-compliant relational database, excellent for complex data relationships and transactional integrity critical for ATS.

Real-time / Messaging

RabbitMQ; Reliable message broker for asynchronous tasks like email notifications, AI processing queues, and external system integrations.

Infrastructure

AWS (ECS Fargate, RDS, S3, SQS); Fully managed services reduce operational overhead, offer scalability, and strong security features.

Authentication

Auth0; Provides enterprise-grade authentication, SSO, and user management out-of-the-box, saving significant development time.

Key third-party services

SendGrid/Mailgun (email delivery), Twilio (SMS notifications), OpenAI API / Hugging Face (AI for parsing/matching), Stripe (payment processing), Google Calendar API / Outlook Calendar API (interview scheduling), LinkedIn API (job board integration).

Core modules

Key data model

Entity	Key fields	Notes
Tenant	id, name, subscription_plan, created_at	Indexed by id
User	id, tenant_id, email, role, password_hash, first_name, last_name	Tenant_id foreign key, indexed by email, unique per tenant
JobPosting	id, tenant_id, title, description, status, department, created_by_user_id, posted_date, closing_date	Tenant_id, created_by_user_id foreign keys, indexed by status and tenant_id
Candidate	id, tenant_id, email, full_name, resume_url, current_status, phone_number, source	Tenant_id foreign key, indexed by email, unique per tenant
Application	id, job_posting_id, candidate_id, current_stage, applied_date, last_status_update, notes	JobPosting_id, Candidate_id foreign keys, composite index on (job_posting_id, current_stage)
Interview	id, application_id, interviewer_user_id, scheduled_time, status, feedback_form_id, location, interview_type	Application_id, Interviewer_user_id foreign keys, indexed by scheduled_time
TalentPool	id, tenant_id, name, description, created_by_user_id	Tenant_id foreign key
CandidateTalentPool	candidate_id, talent_pool_id, added_date	Composite primary key (candidate_id, talent_pool_id), foreign keys to Candidate and TalentPool

Core API endpoints

Method	Endpoint	Purpose
POST	/api/jobs	Create a new job posting for the authenticated tenant
GET	/api/jobs/{id}/applications	Retrieve all applications for a specific job posting, including candidate details
POST	/api/candidates/apply	Submit a new candidate application, potentially with resume upload and parsing
PUT	/api/applications/{id}/status	Update an application's stage in the hiring pipeline (e.g., 'Screened' to 'Interview')
GET	/api/candidates/{id}/history	Fetch a candidate's full application history, communication logs, and interview feedback
POST	/api/interviews/schedule	Schedule an interview for a specific application, inviting interviewers and candidates
GET	/api/reports/time-to-hire	Generate a report on average time-to-hire metrics across different jobs or departments
POST	/api/ai/match-candidates	Trigger AI matching for candidates against a job posting, returning ranked suggestions
GET	/api/talent-pools/{id}/candidates	Retrieve candidates within a specific talent pool, with filtering options
POST	/api/webhooks/job-boards	Receive job application data from integrated external job boards (e.g., LinkedIn, Indeed)

Core screens

Scaling considerations

• Asynchronous Processing for AI/Resume Parsing: Use message queues (RabbitMQ/SQS) to offload heavy AI tasks like resume parsing and candidate matching, preventing synchronous request timeouts and improving responsiveness.
• Database Performance for Large Datasets: Optimize PostgreSQL with proper indexing on foreign keys and frequently queried fields (e.g., application status, job_id) and consider read replicas for reporting dashboards to reduce load on the primary database.
• Real-time Notifications & Webhooks: Utilize WebSockets or server-sent events for instant updates (e.g., new application, interview scheduled) and robust webhook handling for external integrations with retries and dead-letter queues to ensure reliability.
• Tenant Isolation & Multi-tenancy: Ensure data segregation at the database level (e.g., row-level security or separate schemas) and proper authorization checks to prevent data leaks between different client organizations, crucial for an ATS.
• External API Rate Limits: Implement circuit breakers and rate limiting strategies when integrating with third-party job boards, calendar APIs, or communication services to avoid being blocked and ensure consistent service.

Estimated monthly cost

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Suggested build plan

Phase	Timeframe	Deliverables
Phase 1: Core ATS MVP	Weeks 1-8	User authentication, Tenant management, Job Posting CRUD, Basic Candidate profiles, Application stages, Email notifications
Phase 2: Collaboration & Communication	Weeks 9-16	Interview scheduling, Internal notes & feedback, Communication hub (email/SMS), Candidate portal, User roles & permissions
Phase 3: AI & Integrations	Weeks 17-24	AI resume parsing, Candidate matching, Job board integrations (push/pull), Calendar integrations, Basic reporting
Phase 4: Analytics & Refinement	Weeks 25-32	Advanced reporting dashboard, Talent pool management, Performance optimizations, Bug fixes, UI/UX polish, Compliance features

Frequently asked questions

How will we handle multi-tenancy and ensure data isolation for different companies?

We will implement tenant_id on all relevant database tables and enforce it at the application layer through middleware, ensuring each company's data is strictly separated and accessible only by their authorized users.

What's the strategy for integrating with various job boards and career sites?

We'll build a flexible integration module that supports common APIs (e.g., XML feeds, JSON APIs) for pushing job postings and receiving applications, with an extensible architecture to add new integrations as needed.

How will the AI candidate matching evolve beyond basic keyword matching?

Initially, it will use keyword and skill matching. Future iterations will incorporate natural language processing for semantic understanding, contextual relevance, and potentially learning from recruiter feedback to refine matching accuracy.

What kind of reporting and analytics will be available to hiring managers?

Key metrics will include time-to-hire, source-of-hire, candidate conversion rates per stage, diversity metrics, and pipeline velocity, with customizable dashboards and export options for deeper analysis.

How will we ensure the system is compliant with data privacy regulations like GDPR or CCPA?

We'll implement data anonymization, explicit consent mechanisms for candidate data, data retention policies, and robust access controls. Our infrastructure will be hosted in compliant regions, and we'll conduct regular security audits and maintain a privacy-by-design approach.

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