Overview
HR teams were manually screening hundreds of resumes, copy-pasting data into spreadsheets, and answering the same policy questions repeatedly - using four separate tools that had no integration with each other.
Build a production-grade multi-tenant recruitment SaaS with AI at every stage: resume ingestion, candidate scoring, interview scheduling, onboarding, and policy Q&A - all in one platform.
We delivered seven interconnected feature systems on a FastAPI + Next.js 15 stack with PostgreSQL and pgvector. The resume parser extracts 12+ structured fields from PDF and DOCX files using GPT-4o. Candidate ranking scores match percentage, strengths, weaknesses, and missing skills against the job description. A Kanban drag-and-drop pipeline manages hiring stages. The RAG-based HR chatbot ingests policy PDFs, stores embeddings in pgvector, and answers questions with source citations via LangChain. Onboarding templates with task categories and due-date tracking automate new hire workflows. Analytics cover time-to-hire, pipeline conversion, and recruiter performance with CSV export. Full RBAC with four roles: admin, recruiter, hiring manager, viewer.
Seven production-grade feature systems in a single multi-tenant platform - each with its own data model, API surface, and UI. Dockerized with Docker Compose and auto-documented via Swagger/OpenAPI.
Deliverables
- AI resume parsing (GPT-4o, PDF/DOCX)
- Candidate ranking with match scoring
- Kanban pipeline with drag-and-drop
- RAG HR chatbot with pgvector + LangChain
- Employee onboarding workflow engine
- Automated analytics and CSV export
- Full RBAC (4 roles)
- Docker Compose deployment + Swagger docs
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